library(dplyr)
library(pixiedust)
library(lattice)
library(GGally)
#library(ggpubr)
library(psych)
library(ggplot2)
library(GGally)
library(qqplotr)
library(lattice)
library(Rmisc)
library(corrplot)
library(Hmisc)
protein_expression <- read.csv("~/code/scripts/R/stepik_projects/Data_Cortex_Nuclear.csv")
head(protein_expression)
## MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N NR2A_N pAKT_N pBRAF_N
## 1 309_1 0.5036439 0.7471932 0.4301753 2.816329 5.990152 0.2188300 0.1775655
## 2 309_2 0.5146171 0.6890635 0.4117703 2.789514 5.685038 0.2116362 0.1728170
## 3 309_3 0.5091831 0.7302468 0.4183088 2.687201 5.622059 0.2090109 0.1757222
## 4 309_4 0.4421067 0.6170762 0.3586263 2.466947 4.979503 0.2228858 0.1764626
## 5 309_5 0.4349402 0.6174298 0.3588022 2.365785 4.718679 0.2131059 0.1736270
## 6 309_6 0.4475064 0.6281758 0.3673881 2.385939 4.807635 0.2185778 0.1762334
## pCAMKII_N pCREB_N pELK_N pERK_N pJNK_N PKCA_N pMEK_N
## 1 2.373744 0.2322238 1.750936 0.6879062 0.3063817 0.4026984 0.2969273
## 2 2.292150 0.2269721 1.596377 0.6950062 0.2990511 0.3859868 0.2813189
## 3 2.283337 0.2302468 1.561316 0.6773484 0.2912761 0.3810025 0.2817103
## 4 2.152301 0.2070042 1.595086 0.5832768 0.2967287 0.3770870 0.3138320
## 5 2.134014 0.1921579 1.504230 0.5509601 0.2869612 0.3635021 0.2779643
## 6 2.141282 0.1951875 1.442398 0.5663396 0.2898239 0.3638930 0.2668369
## pNR1_N pNR2A_N pNR2B_N pPKCAB_N pRSK_N AKT_N BRAF_N CAMKII_N
## 1 1.0220603 0.6056726 1.877684 2.308745 0.4415994 0.8593658 0.4162891 0.3696080
## 2 0.9566759 0.5875587 1.725774 2.043037 0.4452219 0.8346593 0.4003642 0.3561775
## 3 1.0036350 0.6024488 1.731873 2.017984 0.4676679 0.8143294 0.3998469 0.3680888
## 4 0.8753903 0.5202932 1.566852 2.132754 0.4776707 0.7277046 0.3856387 0.3629700
## 5 0.8649120 0.5079898 1.480059 2.013697 0.4834161 0.6877937 0.3675305 0.3553109
## 6 0.8591209 0.5213066 1.538244 1.968275 0.4959000 0.6724022 0.3694045 0.3571717
## CREB_N ELK_N ERK_N GSK3B_N JNK_N MEK_N TRKA_N RSK_N
## 1 0.1789443 1.866358 3.685247 1.537227 0.2645263 0.3196770 0.8138665 0.1658460
## 2 0.1736797 1.761047 3.485287 1.509249 0.2557270 0.3044187 0.7805042 0.1571935
## 3 0.1739047 1.765544 3.571456 1.501244 0.2596135 0.3117467 0.7851540 0.1608954
## 4 0.1794489 1.286277 2.970137 1.419710 0.2595358 0.2792181 0.7344917 0.1622099
## 5 0.1748355 1.324695 2.896334 1.359876 0.2507050 0.2736672 0.7026991 0.1548274
## 6 0.1797285 1.227450 2.956983 1.447910 0.2508402 0.2840436 0.7043958 0.1568759
## APP_N Bcatenin_N SOD1_N MTOR_N P38_N pMTOR_N DSCR1_N
## 1 0.4539098 3.037621 0.3695096 0.4585385 0.3353358 0.8251920 0.5769155
## 2 0.4309403 2.921882 0.3422793 0.4235599 0.3248347 0.7617176 0.5450973
## 3 0.4231873 2.944136 0.3436962 0.4250048 0.3248517 0.7570308 0.5436197
## 4 0.4106149 2.500204 0.3445093 0.4292113 0.3301208 0.7469798 0.5467626
## 5 0.3985498 2.456560 0.3291258 0.4087552 0.3134148 0.6919565 0.5368605
## 6 0.3910472 2.467133 0.3275978 0.4044899 0.2962764 0.6744186 0.5397231
## AMPKA_N NR2B_N pNUMB_N RAPTOR_N TIAM1_N pP70S6_N NUMB_N
## 1 0.4480993 0.5862714 0.3947213 0.3395706 0.4828639 0.2941698 0.1821505
## 2 0.4208761 0.5450973 0.3682546 0.3219592 0.4545193 0.2764306 0.1820863
## 3 0.4046298 0.5529941 0.3638799 0.3130859 0.4471972 0.2566482 0.1843877
## 4 0.3868603 0.5478485 0.3667707 0.3284919 0.4426497 0.3985340 0.1617677
## 5 0.3608164 0.5128240 0.3515510 0.3122063 0.4190949 0.3934470 0.1602002
## 6 0.3542143 0.5143164 0.3472241 0.3031321 0.4128243 0.3825783 0.1623303
## P70S6_N pGSK3B_N pPKCG_N CDK5_N S6_N ADARB1_N AcetylH3K9_N
## 1 0.8427252 0.1926084 1.443091 0.2947000 0.3546045 1.339070 0.1701188
## 2 0.8476146 0.1948153 1.439460 0.2940598 0.3545483 1.306323 0.1714271
## 3 0.8561658 0.2007373 1.524364 0.3018807 0.3860868 1.279600 0.1854563
## 4 0.7602335 0.1841694 1.612382 0.2963818 0.2906795 1.198765 0.1597991
## 5 0.7681129 0.1857183 1.645807 0.2968294 0.3093450 1.206995 0.1646503
## 6 0.7796946 0.1867930 1.634615 0.2880373 0.3323671 1.123445 0.1756929
## RRP1_N BAX_N ARC_N ERBB4_N nNOS_N Tau_N GFAP_N
## 1 0.1591024 0.1888517 0.1063052 0.1449893 0.1766677 0.1251904 0.1152909
## 2 0.1581289 0.1845700 0.1065922 0.1504709 0.1783090 0.1342751 0.1182345
## 3 0.1486963 0.1905322 0.1083031 0.1453302 0.1762129 0.1325604 0.1177602
## 4 0.1661123 0.1853235 0.1031838 0.1406558 0.1638042 0.1232096 0.1174394
## 5 0.1606870 0.1882214 0.1047838 0.1419830 0.1677096 0.1368377 0.1160478
## 6 0.1505939 0.1838235 0.1064762 0.1395645 0.1748445 0.1305147 0.1152432
## GluR3_N GluR4_N IL1B_N P3525_N pCASP9_N PSD95_N SNCA_N
## 1 0.2280435 0.1427556 0.4309575 0.2475378 1.603310 2.014875 0.1082343
## 2 0.2380731 0.1420366 0.4571562 0.2576322 1.671738 2.004605 0.1097485
## 3 0.2448173 0.1424450 0.5104723 0.2553430 1.663550 2.016831 0.1081962
## 4 0.2349467 0.1450682 0.4309959 0.2511031 1.484624 1.957233 0.1198832
## 5 0.2555277 0.1408705 0.4812265 0.2517730 1.534835 2.009109 0.1195244
## 6 0.2368495 0.1364536 0.4785775 0.2444853 1.507777 2.003535 0.1206872
## Ubiquitin_N pGSK3B_Tyr216_N SHH_N BAD_N BCL2_N pS6_N pCFOS_N
## 1 1.0449792 0.8315565 0.1888517 0.1226520 NA 0.1063052 0.1083359
## 2 1.0098831 0.8492704 0.2004036 0.1166822 NA 0.1065922 0.1043154
## 3 0.9968476 0.8467087 0.1936845 0.1185082 NA 0.1083031 0.1062193
## 4 0.9902247 0.8332768 0.1921119 0.1327812 NA 0.1031838 0.1112620
## 5 0.9977750 0.8786678 0.2056042 0.1299541 NA 0.1047838 0.1106939
## 6 0.9201782 0.8436793 0.1904695 0.1315752 NA 0.1064762 0.1094457
## SYP_N H3AcK18_N EGR1_N H3MeK4_N CaNA_N Genotype Treatment Behavior
## 1 0.4270992 0.1147832 0.1317900 0.1281856 1.675652 Control Memantine C/S
## 2 0.4415813 0.1119735 0.1351030 0.1311187 1.743610 Control Memantine C/S
## 3 0.4357769 0.1118829 0.1333618 0.1274311 1.926427 Control Memantine C/S
## 4 0.3916910 0.1304053 0.1474442 0.1469011 1.700563 Control Memantine C/S
## 5 0.4341538 0.1184814 0.1403143 0.1483799 1.839730 Control Memantine C/S
## 6 0.4398331 0.1166572 0.1407664 0.1421804 1.816389 Control Memantine C/S
## class
## 1 c-CS-m
## 2 c-CS-m
## 3 c-CS-m
## 4 c-CS-m
## 5 c-CS-m
## 6 c-CS-m
str(protein_expression)
## 'data.frame': 1080 obs. of 82 variables:
## $ MouseID : Factor w/ 1080 levels "18899_1","18899_10",..: 46 53 54 55 56 57 58 59 60 47 ...
## $ DYRK1A_N : num 0.504 0.515 0.509 0.442 0.435 ...
## $ ITSN1_N : num 0.747 0.689 0.73 0.617 0.617 ...
## $ BDNF_N : num 0.43 0.412 0.418 0.359 0.359 ...
## $ NR1_N : num 2.82 2.79 2.69 2.47 2.37 ...
## $ NR2A_N : num 5.99 5.69 5.62 4.98 4.72 ...
## $ pAKT_N : num 0.219 0.212 0.209 0.223 0.213 ...
## $ pBRAF_N : num 0.178 0.173 0.176 0.176 0.174 ...
## $ pCAMKII_N : num 2.37 2.29 2.28 2.15 2.13 ...
## $ pCREB_N : num 0.232 0.227 0.23 0.207 0.192 ...
## $ pELK_N : num 1.75 1.6 1.56 1.6 1.5 ...
## $ pERK_N : num 0.688 0.695 0.677 0.583 0.551 ...
## $ pJNK_N : num 0.306 0.299 0.291 0.297 0.287 ...
## $ PKCA_N : num 0.403 0.386 0.381 0.377 0.364 ...
## $ pMEK_N : num 0.297 0.281 0.282 0.314 0.278 ...
## $ pNR1_N : num 1.022 0.957 1.004 0.875 0.865 ...
## $ pNR2A_N : num 0.606 0.588 0.602 0.52 0.508 ...
## $ pNR2B_N : num 1.88 1.73 1.73 1.57 1.48 ...
## $ pPKCAB_N : num 2.31 2.04 2.02 2.13 2.01 ...
## $ pRSK_N : num 0.442 0.445 0.468 0.478 0.483 ...
## $ AKT_N : num 0.859 0.835 0.814 0.728 0.688 ...
## $ BRAF_N : num 0.416 0.4 0.4 0.386 0.368 ...
## $ CAMKII_N : num 0.37 0.356 0.368 0.363 0.355 ...
## $ CREB_N : num 0.179 0.174 0.174 0.179 0.175 ...
## $ ELK_N : num 1.87 1.76 1.77 1.29 1.32 ...
## $ ERK_N : num 3.69 3.49 3.57 2.97 2.9 ...
## $ GSK3B_N : num 1.54 1.51 1.5 1.42 1.36 ...
## $ JNK_N : num 0.265 0.256 0.26 0.26 0.251 ...
## $ MEK_N : num 0.32 0.304 0.312 0.279 0.274 ...
## $ TRKA_N : num 0.814 0.781 0.785 0.734 0.703 ...
## $ RSK_N : num 0.166 0.157 0.161 0.162 0.155 ...
## $ APP_N : num 0.454 0.431 0.423 0.411 0.399 ...
## $ Bcatenin_N : num 3.04 2.92 2.94 2.5 2.46 ...
## $ SOD1_N : num 0.37 0.342 0.344 0.345 0.329 ...
## $ MTOR_N : num 0.459 0.424 0.425 0.429 0.409 ...
## $ P38_N : num 0.335 0.325 0.325 0.33 0.313 ...
## $ pMTOR_N : num 0.825 0.762 0.757 0.747 0.692 ...
## $ DSCR1_N : num 0.577 0.545 0.544 0.547 0.537 ...
## $ AMPKA_N : num 0.448 0.421 0.405 0.387 0.361 ...
## $ NR2B_N : num 0.586 0.545 0.553 0.548 0.513 ...
## $ pNUMB_N : num 0.395 0.368 0.364 0.367 0.352 ...
## $ RAPTOR_N : num 0.34 0.322 0.313 0.328 0.312 ...
## $ TIAM1_N : num 0.483 0.455 0.447 0.443 0.419 ...
## $ pP70S6_N : num 0.294 0.276 0.257 0.399 0.393 ...
## $ NUMB_N : num 0.182 0.182 0.184 0.162 0.16 ...
## $ P70S6_N : num 0.843 0.848 0.856 0.76 0.768 ...
## $ pGSK3B_N : num 0.193 0.195 0.201 0.184 0.186 ...
## $ pPKCG_N : num 1.44 1.44 1.52 1.61 1.65 ...
## $ CDK5_N : num 0.295 0.294 0.302 0.296 0.297 ...
## $ S6_N : num 0.355 0.355 0.386 0.291 0.309 ...
## $ ADARB1_N : num 1.34 1.31 1.28 1.2 1.21 ...
## $ AcetylH3K9_N : num 0.17 0.171 0.185 0.16 0.165 ...
## $ RRP1_N : num 0.159 0.158 0.149 0.166 0.161 ...
## $ BAX_N : num 0.189 0.185 0.191 0.185 0.188 ...
## $ ARC_N : num 0.106 0.107 0.108 0.103 0.105 ...
## $ ERBB4_N : num 0.145 0.15 0.145 0.141 0.142 ...
## $ nNOS_N : num 0.177 0.178 0.176 0.164 0.168 ...
## $ Tau_N : num 0.125 0.134 0.133 0.123 0.137 ...
## $ GFAP_N : num 0.115 0.118 0.118 0.117 0.116 ...
## $ GluR3_N : num 0.228 0.238 0.245 0.235 0.256 ...
## $ GluR4_N : num 0.143 0.142 0.142 0.145 0.141 ...
## $ IL1B_N : num 0.431 0.457 0.51 0.431 0.481 ...
## $ P3525_N : num 0.248 0.258 0.255 0.251 0.252 ...
## $ pCASP9_N : num 1.6 1.67 1.66 1.48 1.53 ...
## $ PSD95_N : num 2.01 2 2.02 1.96 2.01 ...
## $ SNCA_N : num 0.108 0.11 0.108 0.12 0.12 ...
## $ Ubiquitin_N : num 1.045 1.01 0.997 0.99 0.998 ...
## $ pGSK3B_Tyr216_N: num 0.832 0.849 0.847 0.833 0.879 ...
## $ SHH_N : num 0.189 0.2 0.194 0.192 0.206 ...
## $ BAD_N : num 0.123 0.117 0.119 0.133 0.13 ...
## $ BCL2_N : num NA NA NA NA NA NA NA NA NA NA ...
## $ pS6_N : num 0.106 0.107 0.108 0.103 0.105 ...
## $ pCFOS_N : num 0.108 0.104 0.106 0.111 0.111 ...
## $ SYP_N : num 0.427 0.442 0.436 0.392 0.434 ...
## $ H3AcK18_N : num 0.115 0.112 0.112 0.13 0.118 ...
## $ EGR1_N : num 0.132 0.135 0.133 0.147 0.14 ...
## $ H3MeK4_N : num 0.128 0.131 0.127 0.147 0.148 ...
## $ CaNA_N : num 1.68 1.74 1.93 1.7 1.84 ...
## $ Genotype : Factor w/ 2 levels "Control","Ts65Dn": 1 1 1 1 1 1 1 1 1 1 ...
## $ Treatment : Factor w/ 2 levels "Memantine","Saline": 1 1 1 1 1 1 1 1 1 1 ...
## $ Behavior : Factor w/ 2 levels "C/S","S/C": 1 1 1 1 1 1 1 1 1 1 ...
## $ class : Factor w/ 8 levels "c-CS-m","c-CS-s",..: 1 1 1 1 1 1 1 1 1 1 ...
#Определите тип каждого признака (количественные, порядковые, качественные).
В даном датасете все переменные с экспрессией белков — это количественные переменные. Переменные Genotype, Treatment, Behavior, class — качественные.
#Определение размера датасета
dim(protein_expression)
## [1] 1080 82
Я удалила 7 белков так, как по ним было много пропущенных значений, осталось 70 белков. После этого решила удалить некорые строки, где остались NA по других белках, их не так много и наблюдений у нас вполне достаточно. Получила датафрейм в котором 1062 наблюдений и 75 переменных.
colSums(is.na(protein_expression))
## MouseID DYRK1A_N ITSN1_N BDNF_N NR1_N
## 0 3 3 3 3
## NR2A_N pAKT_N pBRAF_N pCAMKII_N pCREB_N
## 3 3 3 3 3
## pELK_N pERK_N pJNK_N PKCA_N pMEK_N
## 3 3 3 3 3
## pNR1_N pNR2A_N pNR2B_N pPKCAB_N pRSK_N
## 3 3 3 3 3
## AKT_N BRAF_N CAMKII_N CREB_N ELK_N
## 3 3 3 3 18
## ERK_N GSK3B_N JNK_N MEK_N TRKA_N
## 3 3 3 7 3
## RSK_N APP_N Bcatenin_N SOD1_N MTOR_N
## 3 3 18 3 3
## P38_N pMTOR_N DSCR1_N AMPKA_N NR2B_N
## 3 3 3 3 3
## pNUMB_N RAPTOR_N TIAM1_N pP70S6_N NUMB_N
## 3 3 3 3 0
## P70S6_N pGSK3B_N pPKCG_N CDK5_N S6_N
## 0 0 0 0 0
## ADARB1_N AcetylH3K9_N RRP1_N BAX_N ARC_N
## 0 0 0 0 0
## ERBB4_N nNOS_N Tau_N GFAP_N GluR3_N
## 0 0 0 0 0
## GluR4_N IL1B_N P3525_N pCASP9_N PSD95_N
## 0 0 0 0 0
## SNCA_N Ubiquitin_N pGSK3B_Tyr216_N SHH_N BAD_N
## 0 0 0 0 213
## BCL2_N pS6_N pCFOS_N SYP_N H3AcK18_N
## 285 0 75 0 180
## EGR1_N H3MeK4_N CaNA_N Genotype Treatment
## 210 270 0 0 0
## Behavior class
## 0 0
protein_expression <- subset(protein_expression, select = -c(BCL2_N, BAD_N, pCFOS_N, H3AcK18_N, EGR1_N, H3MeK4_N, Bcatenin_N))
protein_expression <- na.omit(protein_expression)
На qqplots можно заметить много распределений белков с толстыми хвостами. Решила прологарифмировать распределения, возможно, это логнормальное распределение (?). Для некотрых белков это действительно помогло и стало не идеально, но лучше (DYRK1A_N, ITSN1_N, BDNF_N, …)
sapply(colnames(protein_expression), function(col) {
data <- protein_expression[, col]
if (is.numeric(data)) {
ggplot() + aes(sample = scale(data)) +
stat_qq() +
stat_qq_line() +
ggtitle(col)+
theme_bw()
}
})
## $MouseID
## NULL
##
## $DYRK1A_N
##
## $ITSN1_N
##
## $BDNF_N
##
## $NR1_N
##
## $NR2A_N
##
## $pAKT_N
##
## $pBRAF_N
##
## $pCAMKII_N
##
## $pCREB_N
##
## $pELK_N
##
## $pERK_N
##
## $pJNK_N
##
## $PKCA_N
##
## $pMEK_N
##
## $pNR1_N
##
## $pNR2A_N
##
## $pNR2B_N
##
## $pPKCAB_N
##
## $pRSK_N
##
## $AKT_N
##
## $BRAF_N
##
## $CAMKII_N
##
## $CREB_N
##
## $ELK_N
##
## $ERK_N
##
## $GSK3B_N
##
## $JNK_N
##
## $MEK_N
##
## $TRKA_N
##
## $RSK_N
##
## $APP_N
##
## $SOD1_N
##
## $MTOR_N
##
## $P38_N
##
## $pMTOR_N
##
## $DSCR1_N
##
## $AMPKA_N
##
## $NR2B_N
##
## $pNUMB_N
##
## $RAPTOR_N
##
## $TIAM1_N
##
## $pP70S6_N
##
## $NUMB_N
##
## $P70S6_N
##
## $pGSK3B_N
##
## $pPKCG_N
##
## $CDK5_N
##
## $S6_N
##
## $ADARB1_N
##
## $AcetylH3K9_N
##
## $RRP1_N
##
## $BAX_N
##
## $ARC_N
##
## $ERBB4_N
##
## $nNOS_N
##
## $Tau_N
##
## $GFAP_N
##
## $GluR3_N
##
## $GluR4_N
##
## $IL1B_N
##
## $P3525_N
##
## $pCASP9_N
##
## $PSD95_N
##
## $SNCA_N
##
## $Ubiquitin_N
##
## $pGSK3B_Tyr216_N
##
## $SHH_N
##
## $pS6_N
##
## $SYP_N
##
## $CaNA_N
##
## $Genotype
## NULL
##
## $Treatment
## NULL
##
## $Behavior
## NULL
##
## $class
## NULL
log_qqplot <- function(col) {
ggplot() + aes(sample = scale(log(protein_expression[, col]))) +
stat_qq() +
stat_qq_line() +
ggtitle(col) +
theme_bw()
}
log_qqplot("DYRK1A_N")
log_qqplot("ITSN1_N")
log_qqplot("BDNF_N")
protein_expression[, -c(1, 72:75)] <- log(protein_expression[, -c(1, 72:75)])
## Warning in FUN(X[[i]], ...): NaNs produced
Я построила матрицу корреляций и зачимости корреляции (смотрим по p-values), для этого применила функцию rcorr с использованием корреляции Спирмана, так как в наших распределениях есть аутлаеры (qqplots, скатерплоты).
H0 - корреляции нету между белками; то есть она равно нулю. H1 - корреляция между белками есть; то есть она не равна нулю.
С матриц корреляции и p-value я выбросила по одному белку с каждой пары, где была высокая корреляция (больше 0.8) и она была значима (p-value < 0.05).
#install.packages("corrplot")
matriz <- rcorr(as.matrix(protein_expression[, sapply(protein_expression, is.numeric)]), type=c("spearman"))
mat_corr <- matriz$r
mat_pvalue <- matriz$P
mat_pvalue[is.na(mat_pvalue)] <- 1
which(mat_corr > 0.8 & mat_corr < 1 & mat_pvalue < 0.05, arr.ind=TRUE)
## row col
## ITSN1_N 2 1
## pERK_N 11 1
## BRAF_N 21 1
## DYRK1A_N 1 2
## pERK_N 11 2
## GSK3B_N 26 2
## TRKA_N 29 3
## NR2A_N 5 4
## pNR1_N 15 4
## pNR2B_N 17 4
## ELK_N 24 4
## TRKA_N 29 4
## NR1_N 4 5
## pNR1_N 15 5
## pNR2B_N 17 5
## ELK_N 24 5
## ERK_N 25 5
## pBRAF_N 7 6
## pMEK_N 14 6
## RSK_N 30 6
## pAKT_N 6 7
## pMEK_N 14 7
## CREB_N 23 7
## RSK_N 30 7
## DYRK1A_N 1 11
## ITSN1_N 2 11
## BRAF_N 21 11
## pMEK_N 14 12
## pAKT_N 6 14
## pBRAF_N 7 14
## pJNK_N 12 14
## NR1_N 4 15
## NR2A_N 5 15
## pNR2B_N 17 15
## ELK_N 24 15
## NR1_N 4 17
## NR2A_N 5 17
## pNR1_N 15 17
## ELK_N 24 17
## DYRK1A_N 1 21
## pERK_N 11 21
## RSK_N 30 22
## pBRAF_N 7 23
## RSK_N 30 23
## NR1_N 4 24
## NR2A_N 5 24
## pNR1_N 15 24
## pNR2B_N 17 24
## ERK_N 25 24
## NR2A_N 5 25
## ELK_N 24 25
## ITSN1_N 2 26
## MEK_N 28 27
## JNK_N 27 28
## BDNF_N 3 29
## NR1_N 4 29
## pAKT_N 6 30
## pBRAF_N 7 30
## CAMKII_N 22 30
## CREB_N 23 30
## pMTOR_N 35 33
## NR2B_N 38 33
## MTOR_N 33 35
## NR2B_N 38 35
## TIAM1_N 41 37
## MTOR_N 33 38
## pMTOR_N 35 38
## AMPKA_N 37 41
protein_corr <- which(mat_corr > 0.8 & mat_corr < 1 & mat_pvalue < 0.05, arr.ind=TRUE)
colm <- colnames(mat_corr[, -c(1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 15, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 35, 37, 38, 41)])
colnames(protein_expression[, c(1, 72:75)])
## [1] "MouseID" "Genotype" "Treatment" "Behavior" "class"
protein_expression_0 <- protein_expression
protein_expression <- protein_expression[, c("MouseID", "pCAMKII_N", "pCREB_N", "pELK_N", "PKCA_N", "pNR2A_N", "pPKCAB_N", "pRSK_N", "AKT_N", "APP_N", "SOD1_N", "P38_N", "DSCR1_N", "pNUMB_N", "RAPTOR_N", "pP70S6_N", "NUMB_N", "P70S6_N", "pGSK3B_N", "pPKCG_N", "CDK5_N", "S6_N", "ADARB1_N", "AcetylH3K9_N", "RRP1_N", "BAX_N", "ARC_N", "ERBB4_N", "nNOS_N", "Tau_N", "GFAP_N", "GluR3_N", "GluR4_N", "IL1B_N", "P3525_N", "pCASP9_N", "PSD95_N", "SNCA_N", "Ubiquitin_N", "pGSK3B_Tyr216_N", "SHH_N", "pS6_N", "SYP_N", "CaNA_N", "Genotype", "Treatment", "Behavior", "class")]
#rownames_protein_corr <- row.names(protein_corr)
#protein_expression <- protein_expression[, !(colnames(protein_expression) %in% rownames_protein_corr), drop = FALSE]
Cтатистики по группам, отличия между группами? Одинаковый ли размер этих групп?
Я использовала функцию describeBy, чтобы посмотреть на общие статистики, не учитывая данных с качественными признаками.
head(describe(protein_expression))
## protein_expression
##
## 6 Variables 1062 Observations
## --------------------------------------------------------------------------------
## MouseID
## n missing distinct
## 1062 0 1062
##
## lowest : 18899_1 18899_10 18899_11 18899_12 18899_13
## highest: J3295_5 J3295_6 J3295_7 J3295_8 J3295_9
## --------------------------------------------------------------------------------
## pCAMKII_N
## n missing distinct Info Mean Gmd .05 .10
## 1062 0 1062 1 1.199 0.4275 0.6169 0.7132
## .25 .50 .75 .90 .95
## 0.8990 1.2121 1.5035 1.6851 1.7845
##
## lowest : 0.2956489 0.3147363 0.3320329 0.3381397 0.3470820
## highest: 1.9383517 1.9389091 1.9489805 1.9607484 2.0101009
## --------------------------------------------------------------------------------
## pCREB_N
## n missing distinct Info Mean Gmd .05 .10
## 1062 0 1062 1 -1.56 0.1759 -1.831 -1.766
## .25 .50 .75 .90 .95
## -1.656 -1.557 -1.450 -1.367 -1.319
##
## lowest : -2.182034 -2.060418 -2.059771 -2.019174 -2.003352
## highest: -1.195970 -1.188899 -1.186818 -1.184912 -1.183363
## --------------------------------------------------------------------------------
## pELK_N
## n missing distinct Info Mean Gmd .05 .10
## 1062 0 1062 1 0.3194 0.2664 -0.08757 0.02589
## .25 .50 .75 .90 .95
## 0.18440 0.30329 0.44591 0.57707 0.69437
##
## lowest : -0.8462232 -0.3274397 -0.3089389 -0.2809796 -0.2612155
## highest: 1.5034855 1.5449842 1.5806219 1.5978922 1.8104745
## --------------------------------------------------------------------------------
## PKCA_N
## n missing distinct Info Mean Gmd .05 .10
## 1062 0 1062 1 -1.163 0.1851 -1.4489 -1.3703
## .25 .50 .75 .90 .95
## -1.2674 -1.1646 -1.0484 -0.9461 -0.8971
##
## lowest : -1.6532295 -1.6361521 -1.6276892 -1.6195708 -1.5963125
## highest: -0.7908702 -0.7758301 -0.7704499 -0.7682898 -0.7465648
## --------------------------------------------------------------------------------
## pNR2A_N
## n missing distinct Info Mean Gmd .05 .10
## 1062 0 1062 1 -0.3502 0.3015 -0.82835 -0.71226
## .25 .50 .75 .90 .95
## -0.51860 -0.32393 -0.16179 -0.01821 0.05537
##
## lowest : -1.2683876 -1.1847130 -1.1805198 -1.1624443 -1.1470613
## highest: 0.2556174 0.2579327 0.2922005 0.3074723 0.3455383
## --------------------------------------------------------------------------------
head(describeBy(protein_expression[, -c(1, 45:48)], protein_expression$class, mat=T))
## item group1 vars n mean sd median trimmed
## pCAMKII_N1 1 c-CS-m 1 150 1.0205287 0.3177532 0.9925322 1.0203989
## pCAMKII_N2 2 c-CS-s 1 120 1.0555581 0.2554324 1.0580844 1.0606541
## pCAMKII_N3 3 c-SC-m 1 150 1.5389843 0.1827872 1.5414852 1.5453068
## pCAMKII_N4 4 c-SC-s 1 135 1.1823167 0.2414319 1.1741636 1.1675846
## pCAMKII_N5 5 t-CS-m 1 135 1.0387198 0.4320344 0.8635615 1.0121017
## pCAMKII_N6 6 t-CS-s 1 105 0.8724906 0.2735447 0.8426536 0.8507445
## mad min max range skew kurtosis
## pCAMKII_N1 0.3829263 0.3470820 1.658773 1.3116913 -0.01456047 -0.80900507
## pCAMKII_N2 0.3000264 0.3147363 1.570069 1.2553324 -0.20734179 -0.42103198
## pCAMKII_N3 0.1714655 1.0416770 1.938352 0.8966747 -0.31092393 -0.06749539
## pCAMKII_N4 0.2291153 0.7233370 1.749180 1.0258434 0.48321842 -0.33158121
## pCAMKII_N5 0.2873464 0.3320329 1.857056 1.5250227 0.65675966 -0.91670011
## pCAMKII_N6 0.2173561 0.2956489 1.564750 1.2691012 0.66559855 0.20615950
## se
## pCAMKII_N1 0.02594444
## pCAMKII_N2 0.02331768
## pCAMKII_N3 0.01492452
## pCAMKII_N4 0.02077915
## pCAMKII_N5 0.03718360
## pCAMKII_N6 0.02669523
Решила посмотреть на парные скатерплоты белков, где корреляция очень различается.
pairs(protein_expression[, -c(1, 11:48)], col=protein_expression$class)
ggpairs(protein_expression[, -c(1, 11:48)])
#Не знаю как изменить размер графика и сделать его больше?
#ggpairs(protein_expression, lower = list(combo = wrap("facethist", binwidth = 1, mapping=aes(colour=class, alpha=0.4))))
Для проверки распределения на нормальность используется Shapiro-Wilk normality test или же Mann_Whitney U-test, если есть outliers. H0 – нулевая гипотеза, что наше распределение значимо не отличается от нормального и если мы получаем p-value > 0,05 – это хорошо в нашем случае. H1 – альтернативная гипотеза о том, что наше распределение отличается от нормального.
Я посмотрела на результаты p-value по белках и только по белку pCREB_N (0.067) она сказалась больше 0.05. В всех остальных случаях мы не можем оставить нулевую гипотезу.
Для данных, что я логарифмировала таких значений больше (p-value > 0.05).
#m <- names(diabetes)[sapply(diabetes, is.numeric)]
#sapply(diabetes[, m], shapiro.test)
ans <- rep(NA, ncol(protein_expression))
for (i in 1:ncol(protein_expression)) {
if (is.factor(protein_expression[,i])) next
ans[i] <- shapiro.test(protein_expression[,i])$p.value
}
n <- protein_expression[, -c(1, 45:48)]
s_shap <- sapply(n, shapiro.test)
#s_shap[[1]] ## look at the first column results
Я решила использовать Mann-Whitney U test — непараметрический статистический критерий, используемый для оценки различий между двумя выборками по признаку, измеренному в количественной или порядковой шкале.
Первая выборка — это мыши, которых лечили препаратом Memantine. Вторая выборка — мыши, которых лечили препаратом Saline.
H0: Экспрессия одинакова между группами.
H1: Экспрессия разная.
out <- lapply(2:44, function(x) pairwise.wilcox.test(protein_expression[[x]], protein_expression$Treatment))
names(out) <- names(protein_expression)[2:44]
out
## $pCAMKII_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 6.4e-09
##
## P value adjustment method: holm
##
## $pCREB_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.0054
##
## P value adjustment method: holm
##
## $pELK_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 5.8e-06
##
## P value adjustment method: holm
##
## $PKCA_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.74
##
## P value adjustment method: holm
##
## $pNR2A_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.00012
##
## P value adjustment method: holm
##
## $pPKCAB_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 1.8e-10
##
## P value adjustment method: holm
##
## $pRSK_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.48
##
## P value adjustment method: holm
##
## $AKT_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 5.2e-05
##
## P value adjustment method: holm
##
## $APP_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.82
##
## P value adjustment method: holm
##
## $SOD1_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.015
##
## P value adjustment method: holm
##
## $P38_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 4.7e-11
##
## P value adjustment method: holm
##
## $DSCR1_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 5.7e-08
##
## P value adjustment method: holm
##
## $pNUMB_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.013
##
## P value adjustment method: holm
##
## $RAPTOR_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 1.4e-05
##
## P value adjustment method: holm
##
## $pP70S6_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.75
##
## P value adjustment method: holm
##
## $NUMB_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 9.3e-16
##
## P value adjustment method: holm
##
## $P70S6_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.0065
##
## P value adjustment method: holm
##
## $pGSK3B_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 4.5e-11
##
## P value adjustment method: holm
##
## $pPKCG_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 1e-07
##
## P value adjustment method: holm
##
## $CDK5_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 8.5e-08
##
## P value adjustment method: holm
##
## $S6_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 4.1e-11
##
## P value adjustment method: holm
##
## $ADARB1_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.36
##
## P value adjustment method: holm
##
## $AcetylH3K9_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.00078
##
## P value adjustment method: holm
##
## $RRP1_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 2.5e-07
##
## P value adjustment method: holm
##
## $BAX_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 6.6e-08
##
## P value adjustment method: holm
##
## $ARC_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 8.1e-06
##
## P value adjustment method: holm
##
## $ERBB4_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.11
##
## P value adjustment method: holm
##
## $nNOS_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 3.1e-05
##
## P value adjustment method: holm
##
## $Tau_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.00013
##
## P value adjustment method: holm
##
## $GFAP_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 7.7e-11
##
## P value adjustment method: holm
##
## $GluR3_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 2.2e-06
##
## P value adjustment method: holm
##
## $GluR4_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.13
##
## P value adjustment method: holm
##
## $IL1B_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 2.2e-12
##
## P value adjustment method: holm
##
## $P3525_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 1.1e-11
##
## P value adjustment method: holm
##
## $pCASP9_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 5.6e-05
##
## P value adjustment method: holm
##
## $PSD95_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 7.7e-07
##
## P value adjustment method: holm
##
## $SNCA_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.046
##
## P value adjustment method: holm
##
## $Ubiquitin_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 2.8e-09
##
## P value adjustment method: holm
##
## $pGSK3B_Tyr216_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.13
##
## P value adjustment method: holm
##
## $SHH_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.018
##
## P value adjustment method: holm
##
## $pS6_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 8.1e-06
##
## P value adjustment method: holm
##
## $SYP_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 0.33
##
## P value adjustment method: holm
##
## $CaNA_N
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: protein_expression[[x]] and protein_expression$Treatment
##
## Memantine
## Saline 7.4e-05
##
## P value adjustment method: holm
tests <- sapply(out, function(x) {
p <- x$p.value
p <- pvalString(p, format="exact", digits=4)
n <- outer(rownames(p), colnames(p), paste, sep='v')
p <- as.vector(p)
names(p) <- n
p
})
tests
## pCAMKII_N.SalinevMemantine pCREB_N.SalinevMemantine
## "6.443e-09" "0.0054"
## pELK_N.SalinevMemantine PKCA_N.SalinevMemantine
## "5.798e-06" "0.7424"
## pNR2A_N.SalinevMemantine pPKCAB_N.SalinevMemantine
## "1e-04" "1.77e-10"
## pRSK_N.SalinevMemantine AKT_N.SalinevMemantine
## "0.4824" "5.217e-05"
## APP_N.SalinevMemantine SOD1_N.SalinevMemantine
## "0.8189" "0.0153"
## P38_N.SalinevMemantine DSCR1_N.SalinevMemantine
## "4.673e-11" "5.71e-08"
## pNUMB_N.SalinevMemantine RAPTOR_N.SalinevMemantine
## "0.0129" "1.444e-05"
## pP70S6_N.SalinevMemantine NUMB_N.SalinevMemantine
## "0.7458" "9.26e-16"
## P70S6_N.SalinevMemantine pGSK3B_N.SalinevMemantine
## "0.0065" "4.491e-11"
## pPKCG_N.SalinevMemantine CDK5_N.SalinevMemantine
## "1.021e-07" "8.517e-08"
## S6_N.SalinevMemantine ADARB1_N.SalinevMemantine
## "4.141e-11" "0.3639"
## AcetylH3K9_N.SalinevMemantine RRP1_N.SalinevMemantine
## "8e-04" "2.5e-07"
## BAX_N.SalinevMemantine ARC_N.SalinevMemantine
## "6.621e-08" "8.067e-06"
## ERBB4_N.SalinevMemantine nNOS_N.SalinevMemantine
## "0.1099" "3.113e-05"
## Tau_N.SalinevMemantine GFAP_N.SalinevMemantine
## "1e-04" "7.705e-11"
## GluR3_N.SalinevMemantine GluR4_N.SalinevMemantine
## "2.183e-06" "0.135"
## IL1B_N.SalinevMemantine P3525_N.SalinevMemantine
## "2.208e-12" "1.117e-11"
## pCASP9_N.SalinevMemantine PSD95_N.SalinevMemantine
## "5.606e-05" "7.657e-07"
## SNCA_N.SalinevMemantine Ubiquitin_N.SalinevMemantine
## "0.0456" "2.79e-09"
## pGSK3B_Tyr216_N.SalinevMemantine SHH_N.SalinevMemantine
## "0.1278" "0.0181"
## pS6_N.SalinevMemantine SYP_N.SalinevMemantine
## "8.067e-06" "0.3341"
## CaNA_N.SalinevMemantine
## "7.426e-05"
Для некоторых белков (APP_N, pCREB_N, pP70S6_N, PKCA_N) я получила p-value > 0.05, это дает основания для таких белков отвегнуть Ho о том, что их экспрессия одинакова. Для остальных белков — нету основания отвергнуть Ho о том, что их экспрессия одинаковая.
В матрице корреляций я сохранила значения p-value в переменную mat_pvalue, я не использовала поправку на множественное тестирование, но ее надо использовать, так как возможна ошибка первого рода и можно сделать ложное открытие.
Я провела три теста в предыдущей работе (корреляция, ноормальность и Mann-Whitney test, чтобы определить разная ли экспрессия между разными мышами).
Вот тут я немного не понимаю как считать сколько раз отклонила, для каждого с тестов я для одних белков отклоняла H0, но для других — нет.
Они отличаются тем, что поправка Бенджамини-Хохберга не так сильно влияет на значения p-value.
H0 - корреляции нету между белками; то есть она равно нулю. H1 - корреляция между белками есть; то есть она не равна нулю.
После использования поправок число гипотез для котрых корреляция значима (p-value < 0.05) уменьшилось, в случае с поправкой Бонферрони таких значений ~ на 800 меньше.
# с этой функцией так и не поняла пока что
p_adj_bonf <- corr.test(protein_expression[, sapply(protein_expression, is.numeric)], method="spearman", adjust="bonferroni", alpha=.05)
corr.p(p_adj_bonf$r, p_adj_bonf$n, adjust="bonferroni", alpha=.05)
## Call:corr.p(r = p_adj_bonf$r, n = p_adj_bonf$n, adjust = "bonferroni",
## alpha = 0.05)
## Correlation matrix
## pCAMKII_N pCREB_N pELK_N PKCA_N pNR2A_N pPKCAB_N pRSK_N AKT_N
## pCAMKII_N 1.00 0.43 -0.04 0.35 0.64 0.10 0.45 0.28
## pCREB_N 0.43 1.00 0.31 0.44 0.49 0.22 0.51 0.54
## pELK_N -0.04 0.31 1.00 0.40 0.07 0.35 0.12 0.38
## PKCA_N 0.35 0.44 0.40 1.00 0.27 0.80 0.63 0.34
## pNR2A_N 0.64 0.49 0.07 0.27 1.00 0.00 0.11 0.55
## pPKCAB_N 0.10 0.22 0.35 0.80 0.00 1.00 0.51 0.12
## pRSK_N 0.45 0.51 0.12 0.63 0.11 0.51 1.00 0.12
## AKT_N 0.28 0.54 0.38 0.34 0.55 0.12 0.12 1.00
## APP_N 0.22 0.47 0.49 0.58 0.13 0.46 0.29 0.34
## SOD1_N 0.42 0.47 -0.16 -0.10 0.51 -0.41 0.10 0.53
## P38_N 0.48 0.15 -0.07 0.06 0.45 -0.32 0.01 0.32
## DSCR1_N 0.26 0.33 0.40 0.31 0.34 -0.04 0.07 0.39
## pNUMB_N 0.12 0.21 0.51 0.60 0.21 0.41 0.18 0.30
## RAPTOR_N 0.35 0.29 0.30 0.44 0.41 0.02 0.18 0.43
## pP70S6_N 0.48 0.16 -0.20 0.25 0.02 0.15 0.50 -0.13
## NUMB_N -0.02 0.24 0.17 0.26 0.24 0.42 0.07 0.37
## P70S6_N 0.18 0.26 0.19 0.02 0.47 0.02 -0.08 0.39
## pGSK3B_N -0.23 0.21 0.32 0.44 -0.20 0.50 0.35 0.01
## pPKCG_N 0.34 0.19 -0.26 0.39 -0.06 0.53 0.64 -0.24
## CDK5_N -0.02 0.30 0.15 0.40 0.09 0.47 0.26 0.24
## S6_N -0.15 0.15 0.08 0.12 -0.22 0.35 0.19 -0.05
## ADARB1_N 0.30 0.25 0.30 0.53 0.50 0.45 0.12 0.48
## AcetylH3K9_N 0.20 0.11 -0.20 0.22 -0.07 0.20 0.35 -0.16
## RRP1_N -0.07 0.04 0.02 0.09 -0.04 -0.01 0.08 -0.06
## BAX_N 0.13 0.20 0.12 0.36 0.37 0.38 0.11 0.40
## ARC_N 0.42 0.28 -0.13 -0.09 0.61 -0.36 -0.11 0.52
## ERBB4_N 0.28 0.35 -0.12 0.08 0.41 -0.13 0.10 0.45
## nNOS_N 0.34 0.33 -0.04 0.07 0.43 -0.02 0.10 0.40
## Tau_N 0.03 0.14 0.04 0.00 -0.05 0.04 0.08 0.10
## GFAP_N -0.20 -0.03 0.06 0.13 -0.13 0.08 0.01 -0.08
## GluR3_N 0.08 -0.14 -0.16 -0.02 0.19 -0.07 -0.19 0.01
## GluR4_N 0.15 0.07 -0.05 0.17 0.24 0.08 -0.03 0.18
## IL1B_N 0.15 0.01 -0.39 -0.41 0.23 -0.60 -0.24 0.13
## P3525_N 0.10 0.23 -0.17 0.20 0.10 0.20 0.19 0.13
## pCASP9_N 0.28 0.26 -0.13 0.07 0.48 -0.08 0.08 0.23
## PSD95_N 0.31 0.40 -0.10 -0.07 0.44 -0.16 0.07 0.27
## SNCA_N 0.17 0.04 -0.24 -0.31 0.24 -0.55 -0.23 0.14
## Ubiquitin_N 0.63 0.44 -0.15 0.03 0.58 -0.24 0.16 0.33
## pGSK3B_Tyr216_N 0.06 0.15 0.04 0.30 -0.13 0.39 0.33 0.05
## SHH_N 0.08 0.01 -0.24 -0.12 -0.04 -0.15 -0.08 0.03
## pS6_N 0.42 0.28 -0.13 -0.09 0.61 -0.36 -0.11 0.52
## SYP_N 0.28 0.38 0.03 0.39 0.48 0.17 0.21 0.41
## CaNA_N -0.39 0.03 0.21 0.33 -0.32 0.58 0.19 -0.13
## APP_N SOD1_N P38_N DSCR1_N pNUMB_N RAPTOR_N pP70S6_N NUMB_N
## pCAMKII_N 0.22 0.42 0.48 0.26 0.12 0.35 0.48 -0.02
## pCREB_N 0.47 0.47 0.15 0.33 0.21 0.29 0.16 0.24
## pELK_N 0.49 -0.16 -0.07 0.40 0.51 0.30 -0.20 0.17
## PKCA_N 0.58 -0.10 0.06 0.31 0.60 0.44 0.25 0.26
## pNR2A_N 0.13 0.51 0.45 0.34 0.21 0.41 0.02 0.24
## pPKCAB_N 0.46 -0.41 -0.32 -0.04 0.41 0.02 0.15 0.42
## pRSK_N 0.29 0.10 0.01 0.07 0.18 0.18 0.50 0.07
## AKT_N 0.34 0.53 0.32 0.39 0.30 0.43 -0.13 0.37
## APP_N 1.00 0.00 0.01 0.40 0.51 0.30 0.09 0.31
## SOD1_N 0.00 1.00 0.58 0.32 -0.21 0.37 0.31 -0.10
## P38_N 0.01 0.58 1.00 0.55 0.15 0.68 0.41 -0.40
## DSCR1_N 0.40 0.32 0.55 1.00 0.63 0.80 0.11 -0.18
## pNUMB_N 0.51 -0.21 0.15 0.63 1.00 0.64 -0.06 0.22
## RAPTOR_N 0.30 0.37 0.68 0.80 0.64 1.00 0.25 -0.21
## pP70S6_N 0.09 0.31 0.41 0.11 -0.06 0.25 1.00 -0.31
## NUMB_N 0.31 -0.10 -0.40 -0.18 0.22 -0.21 -0.31 1.00
## P70S6_N 0.16 0.05 -0.09 -0.06 0.08 -0.16 -0.41 0.64
## pGSK3B_N 0.28 -0.34 -0.46 0.02 0.41 0.02 -0.16 0.35
## pPKCG_N 0.16 -0.04 -0.11 -0.25 -0.10 -0.17 0.66 0.08
## CDK5_N 0.34 -0.16 -0.32 -0.01 0.31 -0.06 -0.21 0.58
## S6_N 0.29 -0.33 -0.55 -0.26 0.17 -0.37 -0.17 0.61
## ADARB1_N 0.38 0.05 0.10 0.29 0.45 0.37 -0.10 0.41
## AcetylH3K9_N 0.17 0.11 -0.05 -0.11 0.01 -0.01 0.48 0.13
## RRP1_N 0.01 0.06 0.04 0.27 0.31 0.30 0.07 0.00
## BAX_N 0.26 0.06 -0.13 0.03 0.23 0.08 -0.13 0.65
## ARC_N -0.06 0.58 0.51 0.23 -0.06 0.27 -0.02 0.11
## ERBB4_N 0.04 0.39 0.23 0.13 0.00 0.10 -0.06 0.26
## nNOS_N 0.12 0.35 0.19 -0.06 -0.13 -0.03 0.05 0.39
## Tau_N 0.19 0.14 -0.09 0.02 0.07 -0.05 0.15 0.37
## GFAP_N 0.07 -0.17 -0.11 0.18 0.36 0.19 -0.11 0.11
## GluR3_N -0.07 -0.06 0.06 -0.29 -0.12 -0.17 -0.18 0.21
## GluR4_N 0.23 0.09 0.08 -0.06 0.12 0.03 -0.02 0.37
## IL1B_N -0.30 0.39 0.34 0.02 -0.33 -0.07 -0.11 -0.14
## P3525_N 0.13 0.05 0.08 0.04 0.13 0.04 0.15 0.26
## pCASP9_N 0.03 0.19 0.11 0.00 -0.01 -0.03 -0.16 0.27
## PSD95_N -0.03 0.32 0.06 -0.11 -0.30 -0.19 -0.12 0.27
## SNCA_N -0.24 0.46 0.59 0.36 -0.08 0.32 0.09 -0.33
## Ubiquitin_N 0.00 0.58 0.50 0.37 0.01 0.32 0.23 -0.06
## pGSK3B_Tyr216_N 0.17 -0.20 -0.23 -0.24 -0.05 -0.26 0.02 0.29
## SHH_N -0.05 0.13 0.27 0.00 -0.14 -0.06 0.15 -0.09
## pS6_N -0.06 0.58 0.51 0.23 -0.06 0.27 -0.02 0.11
## SYP_N 0.09 0.21 0.12 0.16 0.21 0.21 -0.16 0.30
## CaNA_N 0.18 -0.62 -0.74 -0.31 0.15 -0.40 -0.39 0.43
## P70S6_N pGSK3B_N pPKCG_N CDK5_N S6_N ADARB1_N AcetylH3K9_N
## pCAMKII_N 0.18 -0.23 0.34 -0.02 -0.15 0.30 0.20
## pCREB_N 0.26 0.21 0.19 0.30 0.15 0.25 0.11
## pELK_N 0.19 0.32 -0.26 0.15 0.08 0.30 -0.20
## PKCA_N 0.02 0.44 0.39 0.40 0.12 0.53 0.22
## pNR2A_N 0.47 -0.20 -0.06 0.09 -0.22 0.50 -0.07
## pPKCAB_N 0.02 0.50 0.53 0.47 0.35 0.45 0.20
## pRSK_N -0.08 0.35 0.64 0.26 0.19 0.12 0.35
## AKT_N 0.39 0.01 -0.24 0.24 -0.05 0.48 -0.16
## APP_N 0.16 0.28 0.16 0.34 0.29 0.38 0.17
## SOD1_N 0.05 -0.34 -0.04 -0.16 -0.33 0.05 0.11
## P38_N -0.09 -0.46 -0.11 -0.32 -0.55 0.10 -0.05
## DSCR1_N -0.06 0.02 -0.25 -0.01 -0.26 0.29 -0.11
## pNUMB_N 0.08 0.41 -0.10 0.31 0.17 0.45 0.01
## RAPTOR_N -0.16 0.02 -0.17 -0.06 -0.37 0.37 -0.01
## pP70S6_N -0.41 -0.16 0.66 -0.21 -0.17 -0.10 0.48
## NUMB_N 0.64 0.35 0.08 0.58 0.61 0.41 0.13
## P70S6_N 1.00 0.02 -0.17 0.36 0.26 0.31 -0.17
## pGSK3B_N 0.02 1.00 0.21 0.56 0.51 0.17 0.21
## pPKCG_N -0.17 0.21 1.00 0.22 0.27 0.04 0.57
## CDK5_N 0.36 0.56 0.22 1.00 0.49 0.34 0.04
## S6_N 0.26 0.51 0.27 0.49 1.00 0.07 0.40
## ADARB1_N 0.31 0.17 0.04 0.34 0.07 1.00 0.03
## AcetylH3K9_N -0.17 0.21 0.57 0.04 0.40 0.03 1.00
## RRP1_N -0.22 0.50 0.05 0.14 0.12 -0.04 0.35
## BAX_N 0.47 0.34 0.12 0.47 0.22 0.49 0.20
## ARC_N 0.41 -0.29 -0.22 0.07 -0.19 0.30 -0.08
## ERBB4_N 0.40 0.02 -0.01 0.32 0.14 0.31 0.08
## nNOS_N 0.55 -0.16 0.07 0.20 0.15 0.18 0.03
## Tau_N 0.19 0.20 0.22 0.17 0.52 0.06 0.61
## GFAP_N -0.17 0.55 -0.02 0.28 0.27 0.06 0.27
## GluR3_N 0.37 -0.11 -0.11 0.17 0.00 0.08 -0.08
## GluR4_N 0.38 0.13 0.06 0.37 0.14 0.21 0.20
## IL1B_N 0.24 -0.35 -0.24 -0.05 -0.13 -0.11 -0.08
## P3525_N 0.06 0.27 0.37 0.44 0.41 0.26 0.35
## pCASP9_N 0.59 0.03 0.02 0.30 0.06 0.29 -0.04
## PSD95_N 0.49 -0.12 0.01 0.26 0.04 0.15 -0.12
## SNCA_N -0.07 -0.26 -0.22 -0.16 -0.33 -0.07 -0.10
## Ubiquitin_N 0.21 -0.19 0.11 0.06 -0.21 0.25 0.02
## pGSK3B_Tyr216_N 0.23 0.35 0.42 0.46 0.34 0.20 0.04
## SHH_N -0.04 -0.10 0.08 0.05 0.03 -0.08 0.10
## pS6_N 0.41 -0.29 -0.22 0.07 -0.19 0.30 -0.08
## SYP_N 0.39 0.22 0.04 0.49 0.03 0.47 -0.07
## CaNA_N 0.14 0.63 0.15 0.60 0.53 0.14 -0.04
## RRP1_N BAX_N ARC_N ERBB4_N nNOS_N Tau_N GFAP_N GluR3_N GluR4_N
## pCAMKII_N -0.07 0.13 0.42 0.28 0.34 0.03 -0.20 0.08 0.15
## pCREB_N 0.04 0.20 0.28 0.35 0.33 0.14 -0.03 -0.14 0.07
## pELK_N 0.02 0.12 -0.13 -0.12 -0.04 0.04 0.06 -0.16 -0.05
## PKCA_N 0.09 0.36 -0.09 0.08 0.07 0.00 0.13 -0.02 0.17
## pNR2A_N -0.04 0.37 0.61 0.41 0.43 -0.05 -0.13 0.19 0.24
## pPKCAB_N -0.01 0.38 -0.36 -0.13 -0.02 0.04 0.08 -0.07 0.08
## pRSK_N 0.08 0.11 -0.11 0.10 0.10 0.08 0.01 -0.19 -0.03
## AKT_N -0.06 0.40 0.52 0.45 0.40 0.10 -0.08 0.01 0.18
## APP_N 0.01 0.26 -0.06 0.04 0.12 0.19 0.07 -0.07 0.23
## SOD1_N 0.06 0.06 0.58 0.39 0.35 0.14 -0.17 -0.06 0.09
## P38_N 0.04 -0.13 0.51 0.23 0.19 -0.09 -0.11 0.06 0.08
## DSCR1_N 0.27 0.03 0.23 0.13 -0.06 0.02 0.18 -0.29 -0.06
## pNUMB_N 0.31 0.23 -0.06 0.00 -0.13 0.07 0.36 -0.12 0.12
## RAPTOR_N 0.30 0.08 0.27 0.10 -0.03 -0.05 0.19 -0.17 0.03
## pP70S6_N 0.07 -0.13 -0.02 -0.06 0.05 0.15 -0.11 -0.18 -0.02
## NUMB_N 0.00 0.65 0.11 0.26 0.39 0.37 0.11 0.21 0.37
## P70S6_N -0.22 0.47 0.41 0.40 0.55 0.19 -0.17 0.37 0.38
## pGSK3B_N 0.50 0.34 -0.29 0.02 -0.16 0.20 0.55 -0.11 0.13
## pPKCG_N 0.05 0.12 -0.22 -0.01 0.07 0.22 -0.02 -0.11 0.06
## CDK5_N 0.14 0.47 0.07 0.32 0.20 0.17 0.28 0.17 0.37
## S6_N 0.12 0.22 -0.19 0.14 0.15 0.52 0.27 0.00 0.14
## ADARB1_N -0.04 0.49 0.30 0.31 0.18 0.06 0.06 0.08 0.21
## AcetylH3K9_N 0.35 0.20 -0.08 0.08 0.03 0.61 0.27 -0.08 0.20
## RRP1_N 1.00 0.18 -0.06 -0.01 -0.23 0.25 0.75 -0.12 0.16
## BAX_N 0.18 1.00 0.23 0.24 0.32 0.23 0.17 0.13 0.37
## ARC_N -0.06 0.23 1.00 0.71 0.57 0.17 -0.05 0.19 0.22
## ERBB4_N -0.01 0.24 0.71 1.00 0.49 0.33 0.06 0.13 0.20
## nNOS_N -0.23 0.32 0.57 0.49 1.00 0.25 -0.27 0.29 0.34
## Tau_N 0.25 0.23 0.17 0.33 0.25 1.00 0.23 -0.14 0.15
## GFAP_N 0.75 0.17 -0.05 0.06 -0.27 0.23 1.00 -0.03 0.17
## GluR3_N -0.12 0.13 0.19 0.13 0.29 -0.14 -0.03 1.00 0.74
## GluR4_N 0.16 0.37 0.22 0.20 0.34 0.15 0.17 0.74 1.00
## IL1B_N -0.06 -0.13 0.65 0.59 0.36 0.11 -0.03 0.29 0.16
## P3525_N 0.19 0.16 0.29 0.53 0.28 0.38 0.31 0.02 0.19
## pCASP9_N -0.04 0.23 0.51 0.58 0.51 0.07 -0.03 0.45 0.44
## PSD95_N -0.21 0.20 0.58 0.61 0.57 0.04 -0.17 0.32 0.24
## SNCA_N 0.27 -0.14 0.59 0.39 0.09 0.03 0.22 -0.06 -0.03
## Ubiquitin_N 0.10 0.14 0.68 0.55 0.37 0.09 -0.02 -0.07 0.04
## pGSK3B_Tyr216_N -0.09 0.26 0.02 0.30 0.29 0.21 -0.01 0.16 0.17
## SHH_N 0.05 -0.09 0.34 0.32 0.22 0.16 0.13 0.06 0.08
## pS6_N -0.06 0.23 1.00 0.71 0.57 0.17 -0.05 0.19 0.22
## SYP_N 0.05 0.36 0.41 0.53 0.31 0.01 0.12 0.24 0.30
## CaNA_N -0.06 0.23 -0.39 -0.04 -0.06 0.05 0.17 0.05 0.03
## IL1B_N P3525_N pCASP9_N PSD95_N SNCA_N Ubiquitin_N
## pCAMKII_N 0.15 0.10 0.28 0.31 0.17 0.63
## pCREB_N 0.01 0.23 0.26 0.40 0.04 0.44
## pELK_N -0.39 -0.17 -0.13 -0.10 -0.24 -0.15
## PKCA_N -0.41 0.20 0.07 -0.07 -0.31 0.03
## pNR2A_N 0.23 0.10 0.48 0.44 0.24 0.58
## pPKCAB_N -0.60 0.20 -0.08 -0.16 -0.55 -0.24
## pRSK_N -0.24 0.19 0.08 0.07 -0.23 0.16
## AKT_N 0.13 0.13 0.23 0.27 0.14 0.33
## APP_N -0.30 0.13 0.03 -0.03 -0.24 0.00
## SOD1_N 0.39 0.05 0.19 0.32 0.46 0.58
## P38_N 0.34 0.08 0.11 0.06 0.59 0.50
## DSCR1_N 0.02 0.04 0.00 -0.11 0.36 0.37
## pNUMB_N -0.33 0.13 -0.01 -0.30 -0.08 0.01
## RAPTOR_N -0.07 0.04 -0.03 -0.19 0.32 0.32
## pP70S6_N -0.11 0.15 -0.16 -0.12 0.09 0.23
## NUMB_N -0.14 0.26 0.27 0.27 -0.33 -0.06
## P70S6_N 0.24 0.06 0.59 0.49 -0.07 0.21
## pGSK3B_N -0.35 0.27 0.03 -0.12 -0.26 -0.19
## pPKCG_N -0.24 0.37 0.02 0.01 -0.22 0.11
## CDK5_N -0.05 0.44 0.30 0.26 -0.16 0.06
## S6_N -0.13 0.41 0.06 0.04 -0.33 -0.21
## ADARB1_N -0.11 0.26 0.29 0.15 -0.07 0.25
## AcetylH3K9_N -0.08 0.35 -0.04 -0.12 -0.10 0.02
## RRP1_N -0.06 0.19 -0.04 -0.21 0.27 0.10
## BAX_N -0.13 0.16 0.23 0.20 -0.14 0.14
## ARC_N 0.65 0.29 0.51 0.58 0.59 0.68
## ERBB4_N 0.59 0.53 0.58 0.61 0.39 0.55
## nNOS_N 0.36 0.28 0.51 0.57 0.09 0.37
## Tau_N 0.11 0.38 0.07 0.04 0.03 0.09
## GFAP_N -0.03 0.31 -0.03 -0.17 0.22 -0.02
## GluR3_N 0.29 0.02 0.45 0.32 -0.06 -0.07
## GluR4_N 0.16 0.19 0.44 0.24 -0.03 0.04
## IL1B_N 1.00 0.30 0.55 0.49 0.59 0.47
## P3525_N 0.30 1.00 0.28 0.22 0.30 0.26
## pCASP9_N 0.55 0.28 1.00 0.53 0.16 0.41
## PSD95_N 0.49 0.22 0.53 1.00 0.22 0.54
## SNCA_N 0.59 0.30 0.16 0.22 1.00 0.59
## Ubiquitin_N 0.47 0.26 0.41 0.54 0.59 1.00
## pGSK3B_Tyr216_N 0.11 0.39 0.32 0.31 -0.14 0.09
## SHH_N 0.49 0.49 0.14 0.13 0.43 0.25
## pS6_N 0.65 0.29 0.51 0.58 0.59 0.68
## SYP_N 0.27 0.35 0.51 0.46 0.16 0.46
## CaNA_N -0.26 0.21 0.05 0.03 -0.55 -0.43
## pGSK3B_Tyr216_N SHH_N pS6_N SYP_N CaNA_N
## pCAMKII_N 0.06 0.08 0.42 0.28 -0.39
## pCREB_N 0.15 0.01 0.28 0.38 0.03
## pELK_N 0.04 -0.24 -0.13 0.03 0.21
## PKCA_N 0.30 -0.12 -0.09 0.39 0.33
## pNR2A_N -0.13 -0.04 0.61 0.48 -0.32
## pPKCAB_N 0.39 -0.15 -0.36 0.17 0.58
## pRSK_N 0.33 -0.08 -0.11 0.21 0.19
## AKT_N 0.05 0.03 0.52 0.41 -0.13
## APP_N 0.17 -0.05 -0.06 0.09 0.18
## SOD1_N -0.20 0.13 0.58 0.21 -0.62
## P38_N -0.23 0.27 0.51 0.12 -0.74
## DSCR1_N -0.24 0.00 0.23 0.16 -0.31
## pNUMB_N -0.05 -0.14 -0.06 0.21 0.15
## RAPTOR_N -0.26 -0.06 0.27 0.21 -0.40
## pP70S6_N 0.02 0.15 -0.02 -0.16 -0.39
## NUMB_N 0.29 -0.09 0.11 0.30 0.43
## P70S6_N 0.23 -0.04 0.41 0.39 0.14
## pGSK3B_N 0.35 -0.10 -0.29 0.22 0.63
## pPKCG_N 0.42 0.08 -0.22 0.04 0.15
## CDK5_N 0.46 0.05 0.07 0.49 0.60
## S6_N 0.34 0.03 -0.19 0.03 0.53
## ADARB1_N 0.20 -0.08 0.30 0.47 0.14
## AcetylH3K9_N 0.04 0.10 -0.08 -0.07 -0.04
## RRP1_N -0.09 0.05 -0.06 0.05 -0.06
## BAX_N 0.26 -0.09 0.23 0.36 0.23
## ARC_N 0.02 0.34 1.00 0.41 -0.39
## ERBB4_N 0.30 0.32 0.71 0.53 -0.04
## nNOS_N 0.29 0.22 0.57 0.31 -0.06
## Tau_N 0.21 0.16 0.17 0.01 0.05
## GFAP_N -0.01 0.13 -0.05 0.12 0.17
## GluR3_N 0.16 0.06 0.19 0.24 0.05
## GluR4_N 0.17 0.08 0.22 0.30 0.03
## IL1B_N 0.11 0.49 0.65 0.27 -0.26
## P3525_N 0.39 0.49 0.29 0.35 0.21
## pCASP9_N 0.32 0.14 0.51 0.51 0.05
## PSD95_N 0.31 0.13 0.58 0.46 0.03
## SNCA_N -0.14 0.43 0.59 0.16 -0.55
## Ubiquitin_N 0.09 0.25 0.68 0.46 -0.43
## pGSK3B_Tyr216_N 1.00 0.30 0.02 0.30 0.49
## SHH_N 0.30 1.00 0.34 0.03 -0.10
## pS6_N 0.02 0.34 1.00 0.41 -0.39
## SYP_N 0.30 0.03 0.41 1.00 0.22
## CaNA_N 0.49 -0.10 -0.39 0.22 1.00
## Sample Size
## pCAMKII_N pCREB_N pELK_N PKCA_N pNR2A_N pPKCAB_N pRSK_N AKT_N
## pCAMKII_N 1062 1062 1062 1062 1062 1062 1062 1062
## pCREB_N 1062 1062 1062 1062 1062 1062 1062 1062
## pELK_N 1062 1062 1062 1062 1062 1062 1062 1062
## PKCA_N 1062 1062 1062 1062 1062 1062 1062 1062
## pNR2A_N 1062 1062 1062 1062 1062 1062 1062 1062
## pPKCAB_N 1062 1062 1062 1062 1062 1062 1062 1062
## pRSK_N 1062 1062 1062 1062 1062 1062 1062 1062
## AKT_N 1062 1062 1062 1062 1062 1062 1062 1062
## APP_N 1062 1062 1062 1062 1062 1062 1062 1062
## SOD1_N 1062 1062 1062 1062 1062 1062 1062 1062
## P38_N 1062 1062 1062 1062 1062 1062 1062 1062
## DSCR1_N 1062 1062 1062 1062 1062 1062 1062 1062
## pNUMB_N 1062 1062 1062 1062 1062 1062 1062 1062
## RAPTOR_N 1062 1062 1062 1062 1062 1062 1062 1062
## pP70S6_N 1062 1062 1062 1062 1062 1062 1062 1062
## NUMB_N 1062 1062 1062 1062 1062 1062 1062 1062
## P70S6_N 1062 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_N 1062 1062 1062 1062 1062 1062 1062 1062
## pPKCG_N 1062 1062 1062 1062 1062 1062 1062 1062
## CDK5_N 1062 1062 1062 1062 1062 1062 1062 1062
## S6_N 1062 1062 1062 1062 1062 1062 1062 1062
## ADARB1_N 1062 1062 1062 1062 1062 1062 1062 1062
## AcetylH3K9_N 1062 1062 1062 1062 1062 1062 1062 1062
## RRP1_N 1061 1061 1061 1061 1061 1061 1061 1061
## BAX_N 1062 1062 1062 1062 1062 1062 1062 1062
## ARC_N 1062 1062 1062 1062 1062 1062 1062 1062
## ERBB4_N 1062 1062 1062 1062 1062 1062 1062 1062
## nNOS_N 1062 1062 1062 1062 1062 1062 1062 1062
## Tau_N 1062 1062 1062 1062 1062 1062 1062 1062
## GFAP_N 1062 1062 1062 1062 1062 1062 1062 1062
## GluR3_N 1062 1062 1062 1062 1062 1062 1062 1062
## GluR4_N 1062 1062 1062 1062 1062 1062 1062 1062
## IL1B_N 1062 1062 1062 1062 1062 1062 1062 1062
## P3525_N 1062 1062 1062 1062 1062 1062 1062 1062
## pCASP9_N 1062 1062 1062 1062 1062 1062 1062 1062
## PSD95_N 1062 1062 1062 1062 1062 1062 1062 1062
## SNCA_N 1062 1062 1062 1062 1062 1062 1062 1062
## Ubiquitin_N 1062 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N 1062 1062 1062 1062 1062 1062 1062 1062
## SHH_N 1062 1062 1062 1062 1062 1062 1062 1062
## pS6_N 1062 1062 1062 1062 1062 1062 1062 1062
## SYP_N 1062 1062 1062 1062 1062 1062 1062 1062
## CaNA_N 1062 1062 1062 1062 1062 1062 1062 1062
## APP_N SOD1_N P38_N DSCR1_N pNUMB_N RAPTOR_N pP70S6_N NUMB_N
## pCAMKII_N 1062 1062 1062 1062 1062 1062 1062 1062
## pCREB_N 1062 1062 1062 1062 1062 1062 1062 1062
## pELK_N 1062 1062 1062 1062 1062 1062 1062 1062
## PKCA_N 1062 1062 1062 1062 1062 1062 1062 1062
## pNR2A_N 1062 1062 1062 1062 1062 1062 1062 1062
## pPKCAB_N 1062 1062 1062 1062 1062 1062 1062 1062
## pRSK_N 1062 1062 1062 1062 1062 1062 1062 1062
## AKT_N 1062 1062 1062 1062 1062 1062 1062 1062
## APP_N 1062 1062 1062 1062 1062 1062 1062 1062
## SOD1_N 1062 1062 1062 1062 1062 1062 1062 1062
## P38_N 1062 1062 1062 1062 1062 1062 1062 1062
## DSCR1_N 1062 1062 1062 1062 1062 1062 1062 1062
## pNUMB_N 1062 1062 1062 1062 1062 1062 1062 1062
## RAPTOR_N 1062 1062 1062 1062 1062 1062 1062 1062
## pP70S6_N 1062 1062 1062 1062 1062 1062 1062 1062
## NUMB_N 1062 1062 1062 1062 1062 1062 1062 1062
## P70S6_N 1062 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_N 1062 1062 1062 1062 1062 1062 1062 1062
## pPKCG_N 1062 1062 1062 1062 1062 1062 1062 1062
## CDK5_N 1062 1062 1062 1062 1062 1062 1062 1062
## S6_N 1062 1062 1062 1062 1062 1062 1062 1062
## ADARB1_N 1062 1062 1062 1062 1062 1062 1062 1062
## AcetylH3K9_N 1062 1062 1062 1062 1062 1062 1062 1062
## RRP1_N 1061 1061 1061 1061 1061 1061 1061 1061
## BAX_N 1062 1062 1062 1062 1062 1062 1062 1062
## ARC_N 1062 1062 1062 1062 1062 1062 1062 1062
## ERBB4_N 1062 1062 1062 1062 1062 1062 1062 1062
## nNOS_N 1062 1062 1062 1062 1062 1062 1062 1062
## Tau_N 1062 1062 1062 1062 1062 1062 1062 1062
## GFAP_N 1062 1062 1062 1062 1062 1062 1062 1062
## GluR3_N 1062 1062 1062 1062 1062 1062 1062 1062
## GluR4_N 1062 1062 1062 1062 1062 1062 1062 1062
## IL1B_N 1062 1062 1062 1062 1062 1062 1062 1062
## P3525_N 1062 1062 1062 1062 1062 1062 1062 1062
## pCASP9_N 1062 1062 1062 1062 1062 1062 1062 1062
## PSD95_N 1062 1062 1062 1062 1062 1062 1062 1062
## SNCA_N 1062 1062 1062 1062 1062 1062 1062 1062
## Ubiquitin_N 1062 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N 1062 1062 1062 1062 1062 1062 1062 1062
## SHH_N 1062 1062 1062 1062 1062 1062 1062 1062
## pS6_N 1062 1062 1062 1062 1062 1062 1062 1062
## SYP_N 1062 1062 1062 1062 1062 1062 1062 1062
## CaNA_N 1062 1062 1062 1062 1062 1062 1062 1062
## P70S6_N pGSK3B_N pPKCG_N CDK5_N S6_N ADARB1_N AcetylH3K9_N
## pCAMKII_N 1062 1062 1062 1062 1062 1062 1062
## pCREB_N 1062 1062 1062 1062 1062 1062 1062
## pELK_N 1062 1062 1062 1062 1062 1062 1062
## PKCA_N 1062 1062 1062 1062 1062 1062 1062
## pNR2A_N 1062 1062 1062 1062 1062 1062 1062
## pPKCAB_N 1062 1062 1062 1062 1062 1062 1062
## pRSK_N 1062 1062 1062 1062 1062 1062 1062
## AKT_N 1062 1062 1062 1062 1062 1062 1062
## APP_N 1062 1062 1062 1062 1062 1062 1062
## SOD1_N 1062 1062 1062 1062 1062 1062 1062
## P38_N 1062 1062 1062 1062 1062 1062 1062
## DSCR1_N 1062 1062 1062 1062 1062 1062 1062
## pNUMB_N 1062 1062 1062 1062 1062 1062 1062
## RAPTOR_N 1062 1062 1062 1062 1062 1062 1062
## pP70S6_N 1062 1062 1062 1062 1062 1062 1062
## NUMB_N 1062 1062 1062 1062 1062 1062 1062
## P70S6_N 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_N 1062 1062 1062 1062 1062 1062 1062
## pPKCG_N 1062 1062 1062 1062 1062 1062 1062
## CDK5_N 1062 1062 1062 1062 1062 1062 1062
## S6_N 1062 1062 1062 1062 1062 1062 1062
## ADARB1_N 1062 1062 1062 1062 1062 1062 1062
## AcetylH3K9_N 1062 1062 1062 1062 1062 1062 1062
## RRP1_N 1061 1061 1061 1061 1061 1061 1061
## BAX_N 1062 1062 1062 1062 1062 1062 1062
## ARC_N 1062 1062 1062 1062 1062 1062 1062
## ERBB4_N 1062 1062 1062 1062 1062 1062 1062
## nNOS_N 1062 1062 1062 1062 1062 1062 1062
## Tau_N 1062 1062 1062 1062 1062 1062 1062
## GFAP_N 1062 1062 1062 1062 1062 1062 1062
## GluR3_N 1062 1062 1062 1062 1062 1062 1062
## GluR4_N 1062 1062 1062 1062 1062 1062 1062
## IL1B_N 1062 1062 1062 1062 1062 1062 1062
## P3525_N 1062 1062 1062 1062 1062 1062 1062
## pCASP9_N 1062 1062 1062 1062 1062 1062 1062
## PSD95_N 1062 1062 1062 1062 1062 1062 1062
## SNCA_N 1062 1062 1062 1062 1062 1062 1062
## Ubiquitin_N 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N 1062 1062 1062 1062 1062 1062 1062
## SHH_N 1062 1062 1062 1062 1062 1062 1062
## pS6_N 1062 1062 1062 1062 1062 1062 1062
## SYP_N 1062 1062 1062 1062 1062 1062 1062
## CaNA_N 1062 1062 1062 1062 1062 1062 1062
## RRP1_N BAX_N ARC_N ERBB4_N nNOS_N Tau_N GFAP_N GluR3_N GluR4_N
## pCAMKII_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pCREB_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pELK_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## PKCA_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pNR2A_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pPKCAB_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pRSK_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## AKT_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## APP_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## SOD1_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## P38_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## DSCR1_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pNUMB_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## RAPTOR_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pP70S6_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## NUMB_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## P70S6_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pPKCG_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## CDK5_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## S6_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## ADARB1_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## AcetylH3K9_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## RRP1_N 1061 1061 1061 1061 1061 1061 1061 1061 1061
## BAX_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## ARC_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## ERBB4_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## nNOS_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## Tau_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## GFAP_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## GluR3_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## GluR4_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## IL1B_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## P3525_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pCASP9_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## PSD95_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## SNCA_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## Ubiquitin_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## SHH_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## pS6_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## SYP_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## CaNA_N 1061 1062 1062 1062 1062 1062 1062 1062 1062
## IL1B_N P3525_N pCASP9_N PSD95_N SNCA_N Ubiquitin_N
## pCAMKII_N 1062 1062 1062 1062 1062 1062
## pCREB_N 1062 1062 1062 1062 1062 1062
## pELK_N 1062 1062 1062 1062 1062 1062
## PKCA_N 1062 1062 1062 1062 1062 1062
## pNR2A_N 1062 1062 1062 1062 1062 1062
## pPKCAB_N 1062 1062 1062 1062 1062 1062
## pRSK_N 1062 1062 1062 1062 1062 1062
## AKT_N 1062 1062 1062 1062 1062 1062
## APP_N 1062 1062 1062 1062 1062 1062
## SOD1_N 1062 1062 1062 1062 1062 1062
## P38_N 1062 1062 1062 1062 1062 1062
## DSCR1_N 1062 1062 1062 1062 1062 1062
## pNUMB_N 1062 1062 1062 1062 1062 1062
## RAPTOR_N 1062 1062 1062 1062 1062 1062
## pP70S6_N 1062 1062 1062 1062 1062 1062
## NUMB_N 1062 1062 1062 1062 1062 1062
## P70S6_N 1062 1062 1062 1062 1062 1062
## pGSK3B_N 1062 1062 1062 1062 1062 1062
## pPKCG_N 1062 1062 1062 1062 1062 1062
## CDK5_N 1062 1062 1062 1062 1062 1062
## S6_N 1062 1062 1062 1062 1062 1062
## ADARB1_N 1062 1062 1062 1062 1062 1062
## AcetylH3K9_N 1062 1062 1062 1062 1062 1062
## RRP1_N 1061 1061 1061 1061 1061 1061
## BAX_N 1062 1062 1062 1062 1062 1062
## ARC_N 1062 1062 1062 1062 1062 1062
## ERBB4_N 1062 1062 1062 1062 1062 1062
## nNOS_N 1062 1062 1062 1062 1062 1062
## Tau_N 1062 1062 1062 1062 1062 1062
## GFAP_N 1062 1062 1062 1062 1062 1062
## GluR3_N 1062 1062 1062 1062 1062 1062
## GluR4_N 1062 1062 1062 1062 1062 1062
## IL1B_N 1062 1062 1062 1062 1062 1062
## P3525_N 1062 1062 1062 1062 1062 1062
## pCASP9_N 1062 1062 1062 1062 1062 1062
## PSD95_N 1062 1062 1062 1062 1062 1062
## SNCA_N 1062 1062 1062 1062 1062 1062
## Ubiquitin_N 1062 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N 1062 1062 1062 1062 1062 1062
## SHH_N 1062 1062 1062 1062 1062 1062
## pS6_N 1062 1062 1062 1062 1062 1062
## SYP_N 1062 1062 1062 1062 1062 1062
## CaNA_N 1062 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N SHH_N pS6_N SYP_N CaNA_N
## pCAMKII_N 1062 1062 1062 1062 1062
## pCREB_N 1062 1062 1062 1062 1062
## pELK_N 1062 1062 1062 1062 1062
## PKCA_N 1062 1062 1062 1062 1062
## pNR2A_N 1062 1062 1062 1062 1062
## pPKCAB_N 1062 1062 1062 1062 1062
## pRSK_N 1062 1062 1062 1062 1062
## AKT_N 1062 1062 1062 1062 1062
## APP_N 1062 1062 1062 1062 1062
## SOD1_N 1062 1062 1062 1062 1062
## P38_N 1062 1062 1062 1062 1062
## DSCR1_N 1062 1062 1062 1062 1062
## pNUMB_N 1062 1062 1062 1062 1062
## RAPTOR_N 1062 1062 1062 1062 1062
## pP70S6_N 1062 1062 1062 1062 1062
## NUMB_N 1062 1062 1062 1062 1062
## P70S6_N 1062 1062 1062 1062 1062
## pGSK3B_N 1062 1062 1062 1062 1062
## pPKCG_N 1062 1062 1062 1062 1062
## CDK5_N 1062 1062 1062 1062 1062
## S6_N 1062 1062 1062 1062 1062
## ADARB1_N 1062 1062 1062 1062 1062
## AcetylH3K9_N 1062 1062 1062 1062 1062
## RRP1_N 1061 1061 1061 1061 1061
## BAX_N 1062 1062 1062 1062 1062
## ARC_N 1062 1062 1062 1062 1062
## ERBB4_N 1062 1062 1062 1062 1062
## nNOS_N 1062 1062 1062 1062 1062
## Tau_N 1062 1062 1062 1062 1062
## GFAP_N 1062 1062 1062 1062 1062
## GluR3_N 1062 1062 1062 1062 1062
## GluR4_N 1062 1062 1062 1062 1062
## IL1B_N 1062 1062 1062 1062 1062
## P3525_N 1062 1062 1062 1062 1062
## pCASP9_N 1062 1062 1062 1062 1062
## PSD95_N 1062 1062 1062 1062 1062
## SNCA_N 1062 1062 1062 1062 1062
## Ubiquitin_N 1062 1062 1062 1062 1062
## pGSK3B_Tyr216_N 1062 1062 1062 1062 1062
## SHH_N 1062 1062 1062 1062 1062
## pS6_N 1062 1062 1062 1062 1062
## SYP_N 1062 1062 1062 1062 1062
## CaNA_N 1062 1062 1062 1062 1062
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## pCAMKII_N pCREB_N pELK_N PKCA_N pNR2A_N pPKCAB_N pRSK_N AKT_N
## pCAMKII_N 0.00 0.00 1.00 0.00 0.00 1.00 0.00 0.00
## pCREB_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## pELK_N 0.16 0.00 0.00 0.00 1.00 0.00 0.10 0.00
## PKCA_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## pNR2A_N 0.00 0.00 0.02 0.00 0.00 1.00 0.19 0.00
## pPKCAB_N 0.00 0.00 0.00 0.00 0.88 0.00 0.00 0.06
## pRSK_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.08
## AKT_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## APP_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## SOD1_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## P38_N 0.00 0.00 0.02 0.05 0.00 0.00 0.78 0.00
## DSCR1_N 0.00 0.00 0.00 0.00 0.00 0.21 0.03 0.00
## pNUMB_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## RAPTOR_N 0.00 0.00 0.00 0.00 0.00 0.42 0.00 0.00
## pP70S6_N 0.00 0.00 0.00 0.00 0.45 0.00 0.00 0.00
## NUMB_N 0.51 0.00 0.00 0.00 0.00 0.00 0.03 0.00
## P70S6_N 0.00 0.00 0.00 0.53 0.00 0.53 0.01 0.00
## pGSK3B_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.83
## pPKCG_N 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.00
## CDK5_N 0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## S6_N 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.09
## ADARB1_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## AcetylH3K9_N 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00
## RRP1_N 0.03 0.20 0.46 0.00 0.20 0.71 0.01 0.05
## BAX_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## ARC_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## ERBB4_N 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00
## nNOS_N 0.00 0.00 0.15 0.03 0.00 0.57 0.00 0.00
## Tau_N 0.41 0.00 0.25 0.91 0.14 0.20 0.01 0.00
## GFAP_N 0.00 0.28 0.05 0.00 0.00 0.01 0.77 0.01
## GluR3_N 0.01 0.00 0.00 0.51 0.00 0.02 0.00 0.64
## GluR4_N 0.00 0.02 0.08 0.00 0.00 0.01 0.28 0.00
## IL1B_N 0.00 0.82 0.00 0.00 0.00 0.00 0.00 0.00
## P3525_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## pCASP9_N 0.00 0.00 0.00 0.02 0.00 0.01 0.01 0.00
## PSD95_N 0.00 0.00 0.00 0.02 0.00 0.00 0.02 0.00
## SNCA_N 0.00 0.15 0.00 0.00 0.00 0.00 0.00 0.00
## Ubiquitin_N 0.00 0.00 0.00 0.29 0.00 0.00 0.00 0.00
## pGSK3B_Tyr216_N 0.05 0.00 0.21 0.00 0.00 0.00 0.00 0.11
## SHH_N 0.01 0.86 0.00 0.00 0.15 0.00 0.01 0.30
## pS6_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## SYP_N 0.00 0.00 0.37 0.00 0.00 0.00 0.00 0.00
## CaNA_N 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.00
## APP_N SOD1_N P38_N DSCR1_N pNUMB_N RAPTOR_N pP70S6_N NUMB_N
## pCAMKII_N 0.00 0.00 0.00 0.00 0.04 0.00 0.00 1.00
## pCREB_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## pELK_N 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00
## PKCA_N 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00
## pNR2A_N 0.02 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## pPKCAB_N 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00
## pRSK_N 0.00 0.94 1.00 1.00 0.00 0.00 0.00 1.00
## AKT_N 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00
## APP_N 0.00 1.00 1.00 0.00 0.00 0.00 1.00 0.00
## SOD1_N 0.97 0.00 0.00 0.00 0.00 0.00 0.00 0.98
## P38_N 0.70 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## DSCR1_N 0.00 0.00 0.00 0.00 0.00 0.00 0.17 0.00
## pNUMB_N 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## RAPTOR_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## pP70S6_N 0.00 0.00 0.00 0.00 0.06 0.00 0.00 0.00
## NUMB_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## P70S6_N 0.00 0.08 0.00 0.04 0.01 0.00 0.00 0.00
## pGSK3B_N 0.00 0.00 0.00 0.57 0.00 0.49 0.00 0.00
## pPKCG_N 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.01
## CDK5_N 0.00 0.00 0.00 0.79 0.00 0.03 0.00 0.00
## S6_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## ADARB1_N 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00
## AcetylH3K9_N 0.00 0.00 0.10 0.00 0.77 0.67 0.00 0.00
## RRP1_N 0.73 0.07 0.17 0.00 0.00 0.00 0.02 0.97
## BAX_N 0.00 0.04 0.00 0.40 0.00 0.01 0.00 0.00
## ARC_N 0.07 0.00 0.00 0.00 0.07 0.00 0.47 0.00
## ERBB4_N 0.19 0.00 0.00 0.00 0.94 0.00 0.05 0.00
## nNOS_N 0.00 0.00 0.00 0.06 0.00 0.27 0.11 0.00
## Tau_N 0.00 0.00 0.01 0.43 0.03 0.09 0.00 0.00
## GFAP_N 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## GluR3_N 0.02 0.05 0.07 0.00 0.00 0.00 0.00 0.00
## GluR4_N 0.00 0.00 0.01 0.05 0.00 0.37 0.49 0.00
## IL1B_N 0.00 0.00 0.00 0.45 0.00 0.03 0.00 0.00
## P3525_N 0.00 0.09 0.01 0.19 0.00 0.24 0.00 0.00
## pCASP9_N 0.38 0.00 0.00 0.88 0.72 0.41 0.00 0.00
## PSD95_N 0.30 0.00 0.05 0.00 0.00 0.00 0.00 0.00
## SNCA_N 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00
## Ubiquitin_N 0.97 0.00 0.00 0.00 0.71 0.00 0.00 0.06
## pGSK3B_Tyr216_N 0.00 0.00 0.00 0.00 0.08 0.00 0.43 0.00
## SHH_N 0.09 0.00 0.00 0.91 0.00 0.05 0.00 0.00
## pS6_N 0.07 0.00 0.00 0.00 0.07 0.00 0.47 0.00
## SYP_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## CaNA_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## P70S6_N pGSK3B_N pPKCG_N CDK5_N S6_N ADARB1_N AcetylH3K9_N
## pCAMKII_N 0.00 0.00 0.00 1.00 0.00 0.00 0.00
## pCREB_N 0.00 0.00 0.00 0.00 0.00 0.00 0.28
## pELK_N 0.00 0.00 0.00 0.00 1.00 0.00 0.00
## PKCA_N 1.00 0.00 0.00 0.00 0.06 0.00 0.00
## pNR2A_N 0.00 0.00 1.00 1.00 0.00 0.00 1.00
## pPKCAB_N 1.00 0.00 0.00 0.00 0.00 0.00 0.00
## pRSK_N 1.00 0.00 0.00 0.00 0.00 0.06 0.00
## AKT_N 0.00 1.00 0.00 0.00 1.00 0.00 0.00
## APP_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## SOD1_N 1.00 0.00 1.00 0.00 0.00 1.00 0.23
## P38_N 1.00 0.00 0.30 0.00 0.00 0.74 1.00
## DSCR1_N 1.00 1.00 0.00 1.00 0.00 0.00 0.38
## pNUMB_N 1.00 0.00 0.91 0.00 0.00 0.00 1.00
## RAPTOR_N 0.00 1.00 0.00 1.00 0.00 0.00 1.00
## pP70S6_N 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## NUMB_N 0.00 0.00 1.00 0.00 0.00 0.00 0.01
## P70S6_N 0.00 1.00 0.00 0.00 0.00 0.00 0.00
## pGSK3B_N 0.46 0.00 0.00 0.00 0.00 0.00 0.00
## pPKCG_N 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## CDK5_N 0.00 0.00 0.00 0.00 0.00 0.00 1.00
## S6_N 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## ADARB1_N 0.00 0.00 0.16 0.00 0.03 0.00 1.00
## AcetylH3K9_N 0.00 0.00 0.00 0.22 0.00 0.26 0.00
## RRP1_N 0.00 0.00 0.14 0.00 0.00 0.20 0.00
## BAX_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## ARC_N 0.00 0.00 0.00 0.03 0.00 0.00 0.01
## ERBB4_N 0.00 0.43 0.71 0.00 0.00 0.00 0.01
## nNOS_N 0.00 0.00 0.02 0.00 0.00 0.00 0.33
## Tau_N 0.00 0.00 0.00 0.00 0.00 0.04 0.00
## GFAP_N 0.00 0.00 0.42 0.00 0.00 0.04 0.00
## GluR3_N 0.00 0.00 0.00 0.00 0.90 0.01 0.01
## GluR4_N 0.00 0.00 0.06 0.00 0.00 0.00 0.00
## IL1B_N 0.00 0.00 0.00 0.10 0.00 0.00 0.01
## P3525_N 0.04 0.00 0.00 0.00 0.00 0.00 0.00
## pCASP9_N 0.00 0.27 0.50 0.00 0.05 0.00 0.23
## PSD95_N 0.00 0.00 0.68 0.00 0.17 0.00 0.00
## SNCA_N 0.02 0.00 0.00 0.00 0.00 0.02 0.00
## Ubiquitin_N 0.00 0.00 0.00 0.05 0.00 0.00 0.58
## pGSK3B_Tyr216_N 0.00 0.00 0.00 0.00 0.00 0.00 0.17
## SHH_N 0.17 0.00 0.01 0.09 0.41 0.01 0.00
## pS6_N 0.00 0.00 0.00 0.03 0.00 0.00 0.01
## SYP_N 0.00 0.00 0.20 0.00 0.37 0.00 0.02
## CaNA_N 0.00 0.00 0.00 0.00 0.00 0.00 0.21
## RRP1_N BAX_N ARC_N ERBB4_N nNOS_N Tau_N GFAP_N GluR3_N GluR4_N
## pCAMKII_N 1.00 0.01 0.00 0.00 0.00 1.00 0.00 1.00 0.00
## pCREB_N 1.00 0.00 0.00 0.00 0.00 0.01 1.00 0.00 1.00
## pELK_N 1.00 0.06 0.02 0.04 1.00 1.00 1.00 0.00 1.00
## PKCA_N 1.00 0.00 1.00 1.00 1.00 1.00 0.02 1.00 0.00
## pNR2A_N 1.00 0.00 0.00 0.00 0.00 1.00 0.03 0.00 0.00
## pPKCAB_N 1.00 0.00 0.00 0.02 1.00 1.00 1.00 1.00 1.00
## pRSK_N 1.00 0.16 0.36 1.00 0.75 1.00 1.00 0.00 1.00
## AKT_N 1.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 0.00
## APP_N 1.00 0.00 1.00 1.00 0.13 0.00 1.00 1.00 0.00
## SOD1_N 1.00 1.00 0.00 0.00 0.00 0.01 0.00 1.00 1.00
## P38_N 1.00 0.03 0.00 0.00 0.00 1.00 0.32 1.00 1.00
## DSCR1_N 0.00 1.00 0.00 0.02 1.00 1.00 0.00 0.00 1.00
## pNUMB_N 0.00 0.00 1.00 1.00 0.03 1.00 0.00 0.10 0.09
## RAPTOR_N 0.00 1.00 0.00 0.57 1.00 1.00 0.00 0.00 1.00
## pP70S6_N 1.00 0.01 1.00 1.00 1.00 0.00 0.36 0.00 1.00
## NUMB_N 1.00 0.00 0.16 0.00 0.00 0.00 0.21 0.00 0.00
## P70S6_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## pGSK3B_N 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.55 0.02
## pPKCG_N 1.00 0.13 0.00 1.00 1.00 0.00 1.00 0.29 1.00
## CDK5_N 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00
## S6_N 0.06 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00
## ADARB1_N 1.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 0.00
## AcetylH3K9_N 0.00 0.00 1.00 1.00 1.00 0.00 0.00 1.00 0.00
## RRP1_N 0.00 0.00 1.00 1.00 0.00 0.00 0.00 0.11 0.00
## BAX_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00
## ARC_N 0.06 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00
## ERBB4_N 0.74 0.00 0.00 0.00 0.00 0.00 1.00 0.03 0.00
## nNOS_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## Tau_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## GFAP_N 0.00 0.00 0.10 0.04 0.00 0.00 0.00 1.00 0.00
## GluR3_N 0.00 0.00 0.00 0.00 0.00 0.00 0.29 0.00 0.00
## GluR4_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## IL1B_N 0.03 0.00 0.00 0.00 0.00 0.00 0.28 0.00 0.00
## P3525_N 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.51 0.00
## pCASP9_N 0.19 0.00 0.00 0.00 0.00 0.03 0.33 0.00 0.00
## PSD95_N 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.00 0.00
## SNCA_N 0.00 0.00 0.00 0.00 0.00 0.35 0.00 0.03 0.28
## Ubiquitin_N 0.00 0.00 0.00 0.00 0.00 0.00 0.57 0.02 0.20
## pGSK3B_Tyr216_N 0.00 0.00 0.60 0.00 0.00 0.00 0.70 0.00 0.00
## SHH_N 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.01
## pS6_N 0.06 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00
## SYP_N 0.12 0.00 0.00 0.00 0.00 0.71 0.00 0.00 0.00
## CaNA_N 0.05 0.00 0.00 0.24 0.07 0.14 0.00 0.08 0.34
## IL1B_N P3525_N pCASP9_N PSD95_N SNCA_N Ubiquitin_N
## pCAMKII_N 0.00 0.99 0.00 0.00 0 0.00
## pCREB_N 1.00 0.00 0.00 0.00 1 0.00
## pELK_N 0.00 0.00 0.01 0.71 0 0.00
## PKCA_N 0.00 0.00 1.00 1.00 0 1.00
## pNR2A_N 0.00 0.64 0.00 0.00 0 0.00
## pPKCAB_N 0.00 0.00 1.00 0.00 0 0.00
## pRSK_N 0.00 0.00 1.00 1.00 0 0.00
## AKT_N 0.03 0.03 0.00 0.00 0 0.00
## APP_N 0.00 0.01 1.00 1.00 0 1.00
## SOD1_N 0.00 1.00 0.00 0.00 0 0.00
## P38_N 0.00 1.00 0.22 1.00 0 0.00
## DSCR1_N 1.00 1.00 1.00 0.19 0 0.00
## pNUMB_N 0.00 0.02 1.00 0.00 1 1.00
## RAPTOR_N 1.00 1.00 1.00 0.00 0 0.00
## pP70S6_N 0.19 0.00 0.00 0.09 1 0.00
## NUMB_N 0.00 0.00 0.00 0.00 0 1.00
## P70S6_N 0.00 1.00 0.00 0.00 1 0.00
## pGSK3B_N 0.00 0.00 1.00 0.06 0 0.00
## pPKCG_N 0.00 0.00 1.00 1.00 0 0.46
## CDK5_N 1.00 0.00 0.00 0.00 0 1.00
## S6_N 0.01 0.00 1.00 1.00 0 0.00
## ADARB1_N 0.44 0.00 0.00 0.00 1 0.00
## AcetylH3K9_N 1.00 0.00 1.00 0.05 1 1.00
## RRP1_N 1.00 0.00 1.00 0.00 0 1.00
## BAX_N 0.03 0.00 0.00 0.00 0 0.00
## ARC_N 0.00 0.00 0.00 0.00 0 0.00
## ERBB4_N 0.00 0.00 0.00 0.00 0 0.00
## nNOS_N 0.00 0.00 0.00 0.00 1 0.00
## Tau_N 0.21 0.00 1.00 1.00 1 1.00
## GFAP_N 1.00 0.00 1.00 0.00 0 1.00
## GluR3_N 0.00 1.00 0.00 0.00 1 1.00
## GluR4_N 0.00 0.00 0.00 0.00 1 1.00
## IL1B_N 0.00 0.00 0.00 0.00 0 0.00
## P3525_N 0.00 0.00 0.00 0.00 0 0.00
## pCASP9_N 0.00 0.00 0.00 0.00 0 0.00
## PSD95_N 0.00 0.00 0.00 0.00 0 0.00
## SNCA_N 0.00 0.00 0.00 0.00 0 0.00
## Ubiquitin_N 0.00 0.00 0.00 0.00 0 0.00
## pGSK3B_Tyr216_N 0.00 0.00 0.00 0.00 0 0.00
## SHH_N 0.00 0.00 0.00 0.00 0 0.00
## pS6_N 0.00 0.00 0.00 0.00 0 0.00
## SYP_N 0.00 0.00 0.00 0.00 0 0.00
## CaNA_N 0.00 0.00 0.09 0.38 0 0.00
## pGSK3B_Tyr216_N SHH_N pS6_N SYP_N CaNA_N
## pCAMKII_N 1.00 1.00 0.00 0.00 0.00
## pCREB_N 0.00 1.00 0.00 0.00 1.00
## pELK_N 1.00 0.00 0.02 1.00 0.00
## PKCA_N 0.00 0.05 1.00 0.00 0.00
## pNR2A_N 0.04 1.00 0.00 0.00 0.00
## pPKCAB_N 0.00 0.00 0.00 0.00 0.00
## pRSK_N 0.00 1.00 0.36 0.00 0.00
## AKT_N 1.00 1.00 0.00 0.00 0.03
## APP_N 0.00 1.00 1.00 1.00 0.00
## SOD1_N 0.00 0.02 0.00 0.00 0.00
## P38_N 0.00 0.00 0.00 0.13 0.00
## DSCR1_N 0.00 1.00 0.00 0.00 0.00
## pNUMB_N 1.00 0.00 1.00 0.00 0.00
## RAPTOR_N 0.00 1.00 0.00 0.00 0.00
## pP70S6_N 1.00 0.00 1.00 0.00 0.00
## NUMB_N 0.00 1.00 0.16 0.00 0.00
## P70S6_N 0.00 1.00 0.00 0.00 0.01
## pGSK3B_N 0.00 0.79 0.00 0.00 0.00
## pPKCG_N 0.00 1.00 0.00 1.00 0.00
## CDK5_N 0.00 1.00 1.00 0.00 0.00
## S6_N 0.00 1.00 0.00 1.00 0.00
## ADARB1_N 0.00 1.00 0.00 0.00 0.01
## AcetylH3K9_N 1.00 1.00 1.00 1.00 1.00
## RRP1_N 1.00 1.00 1.00 1.00 1.00
## BAX_N 0.00 1.00 0.00 0.00 0.00
## ARC_N 1.00 0.00 0.00 0.00 0.00
## ERBB4_N 0.00 0.00 0.00 0.00 1.00
## nNOS_N 0.00 0.00 0.00 0.00 1.00
## Tau_N 0.00 0.00 0.00 1.00 1.00
## GFAP_N 1.00 0.02 1.00 0.11 0.00
## GluR3_N 0.00 1.00 0.00 0.00 1.00
## GluR4_N 0.00 1.00 0.00 0.00 1.00
## IL1B_N 0.31 0.00 0.00 0.00 0.00
## P3525_N 0.00 0.00 0.00 0.00 0.00
## pCASP9_N 0.00 0.01 0.00 0.00 1.00
## PSD95_N 0.00 0.01 0.00 0.00 1.00
## SNCA_N 0.00 0.00 0.00 0.00 0.00
## Ubiquitin_N 1.00 0.00 0.00 0.00 0.00
## pGSK3B_Tyr216_N 0.00 0.00 1.00 0.00 0.00
## SHH_N 0.00 0.00 0.00 1.00 0.62
## pS6_N 0.60 0.00 0.00 0.00 0.00
## SYP_N 0.00 0.38 0.00 0.00 0.00
## CaNA_N 0.00 0.00 0.00 0.00 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option
pvalue_bonferroni_cor <- p_adj_bonf$p
sum(pvalue_bonferroni_cor <0.05)
## [1] 1406
#поправка Бонферрони
p_adj_bonf_0 <- p.adjust(mat_pvalue, method="bonferroni")
sum(p_adj_bonf_0 < 0.05)
## [1] 3370
#поправка Бенджамини-Хохберга
p_adj_BH <- p.adjust(mat_pvalue, method = "BH")
sum(p_adj_BH < 0.05)
## [1] 4126
sum(mat_pvalue < 0.05)
## [1] 4154
Я провела однофакторный дисперсионный анализ между независимой переменной class, которая делит наблюдения на группы и зависимой переменной — уровнем экспрессии белка pCAMKII_N. Я получила довольно большое значение F и очень маленькое p-value. Можно сказать, что есть статистическая разница между группами.
Hypothesis in one-way ANOVA test:
H0: The means between groups are identical
H1: At least, the mean of one group is different
Я провела множественное сравнение в ANOVA для независимых переменных Genotype, Treatment и зависимой — уровнем экспрессии белка pCAMKII_N.
С результатов можно сделать выводы:
Hypothesis in two-way ANOVA test:
H0: The means are equal for both variables (i.e., factor variable)
H1: The means are different for both variables
anova_one_way <- aov(protein_expression$pCAMKII_N~class, data = protein_expression)
summary(anova_one_way)
## Df Sum Sq Mean Sq F value Pr(>F)
## class 7 49.33 7.047 76.44 <2e-16 ***
## Residuals 1054 97.17 0.092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova_two_way <- aov(protein_expression$pCAMKII_N~Genotype + Treatment, data = protein_expression)
summary(anova_two_way)
## Df Sum Sq Mean Sq F value Pr(>F)
## Genotype 1 0.09 0.087 0.644 0.422
## Treatment 1 4.02 4.023 29.923 5.61e-08 ***
## Residuals 1059 142.38 0.134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tuk_one_way_anova <- TukeyHSD(anova_one_way)
tuk_one_way_anova
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = protein_expression$pCAMKII_N ~ class, data = protein_expression)
##
## $class
## diff lwr upr p adj
## c-CS-s-c-CS-m 0.03502945 -0.07790533 0.14796422 0.9818426
## c-SC-m-c-CS-m 0.51845562 0.41197970 0.62493155 0.0000000
## c-SC-s-c-CS-m 0.16178803 0.05239441 0.27118164 0.0002106
## t-CS-m-c-CS-m 0.01819110 -0.09120252 0.12758471 0.9996388
## t-CS-s-c-CS-m -0.14803808 -0.26536884 -0.03070733 0.0033689
## t-SC-m-c-CS-m 0.40078054 0.29138693 0.51017415 0.0000000
## t-SC-s-c-CS-m 0.33825365 0.22820772 0.44829959 0.0000000
## c-SC-m-c-CS-s 0.48342618 0.37049140 0.59636095 0.0000000
## c-SC-s-c-CS-s 0.12675858 0.01106890 0.24244827 0.0203559
## t-CS-m-c-CS-s -0.01683835 -0.13252804 0.09885133 0.9998516
## t-CS-s-c-CS-s -0.18306753 -0.30628947 -0.05984559 0.0001919
## t-SC-m-c-CS-s 0.36575109 0.25006141 0.48144078 0.0000000
## t-SC-s-c-CS-s 0.30322420 0.18691750 0.41953090 0.0000000
## c-SC-s-c-SC-m -0.35666760 -0.46606121 -0.24727398 0.0000000
## t-CS-m-c-SC-m -0.50026453 -0.60965814 -0.39087091 0.0000000
## t-CS-s-c-SC-m -0.66649371 -0.78382446 -0.54916295 0.0000000
## t-SC-m-c-SC-m -0.11767508 -0.22706870 -0.00828147 0.0247700
## t-SC-s-c-SC-m -0.18020197 -0.29024791 -0.07015604 0.0000211
## t-CS-m-c-SC-s -0.14359693 -0.25583241 -0.03136145 0.0027341
## t-CS-s-c-SC-s -0.30982611 -0.42981089 -0.18984134 0.0000000
## t-SC-m-c-SC-s 0.23899251 0.12675703 0.35122799 0.0000000
## t-SC-s-c-SC-s 0.17646562 0.06359424 0.28933700 0.0000636
## t-CS-s-t-CS-m -0.16622918 -0.28621395 -0.04624441 0.0007344
## t-SC-m-t-CS-m 0.38258945 0.27035397 0.49482492 0.0000000
## t-SC-s-t-CS-m 0.32006256 0.20719118 0.43293394 0.0000000
## t-SC-m-t-CS-s 0.54881863 0.42883385 0.66880340 0.0000000
## t-SC-s-t-CS-s 0.48629174 0.36571192 0.60687155 0.0000000
## t-SC-s-t-SC-m -0.06252689 -0.17539827 0.05034449 0.6987968
tuk_two_way_anova <- TukeyHSD(anova_two_way)
tuk_two_way_anova
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = protein_expression$pCAMKII_N ~ Genotype + Treatment, data = protein_expression)
##
## $Genotype
## diff lwr upr p adj
## Ts65Dn-Control -0.01808316 -0.0622847 0.02611837 0.4222978
##
## $Treatment
## diff lwr upr p adj
## Saline-Memantine -0.1234264 -0.1677024 -0.07915048 1e-07
plot(tuk_one_way_anova)
plot(tuk_two_way_anova)